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Tianrui Li

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21 papers

1 author row

AIIM Journal 2026 Journal Article

Tackling small sample survival analysis via transfer learning: A study of colorectal cancer prognosis

Yonghao Zhao
Changtao Li
Chi Shu
Qingbin Wu
Hong Li
Chuan Xu
Tianrui Li
Ziqiang Wang

Survival prognosis is crucial for medical informatics. Practitioners often confront small-sized clinical data, especially cancer patient cases, which can be insufficient to induce useful patterns for survival predictions. This study deals with small sample survival analysis by leveraging transfer learning, a useful machine learning technique that can enhance the target analysis with related knowledge pre-learned from other data. We propose and develop various transfer learning methods designed for common survival models. For parametric models such as DeepSurv, Cox-CC (Cox-based neural networks), and DeepHit (end-to-end deep learning model), we apply standard transfer learning techniques like pretraining and fine-tuning. For non-parametric models such as Random Survival Forest, we propose a new transfer survival forest (TSF) model that transfers tree structures from source tasks and fine-tunes them with target data. We evaluated the transfer learning methods on colorectal cancer (CRC) prognosis. The source data are 27, 379 SEER CRC stage I patients, and the target data are 728 CRC stage I patients from the West China Hospital. When enhanced by transfer learning, Cox-CC’s C t d value was boosted from 0. 7868 to 0. 8111, DeepHit’s from 0. 8085 to 0. 8135, DeepSurv’s from 0. 7722 to 0. 8043, and RSF’s from 0. 7940 to 0. 8297 (the highest performance). All models trained with data as small as 50 demonstrated even more significant improvement. Conclusions: Therefore, the current survival models used for cancer prognosis can be enhanced and improved by properly designed transfer learning techniques. The source code used in this study is available at https: //github. com/YonghaoZhao722/TSF.

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NeurIPS Conference 2025 Conference Paper

Efficiently Maintaining the Multilingual Capacity of MCLIP in Downstream Cross-Modal Retrieval Tasks

Fengmao Lyu
Jitong Lei
Guosheng Lin
Desheng ZHENG
Jianyang Zhang
Tianrui Li

While existing research on Multilingual CLIP (MCLIP) has prioritized model architecture design, our work uncovers a critical challenge in practical adaptation: fine-tuning MCLIP through a single source language risks diminishing its multilingual capabilities in downstream tasks due to cross-linguistic disparities. To bridge this gap, we systematically investigate the role of token similarity in cross-lingual transferability for image-text retrieval, establishing it as a key factor governing fine-tuning efficacy. Building on this insight, we propose two novel strategies to enhance efficiency while preserving multilinguality: 1) TaPCL dynamically optimizes training by prioritizing linguistically distant language pairs during corpus sampling, reducing redundant computation, and 2) CiPCL enriches the source corpus with multilingual key terms, enabling targeted knowledge transfer without reliance on exhaustive parallel data. By strategically balancing token similarity and domain-critical information, our methods significantly lower computational costs and mitigate over-dependence on parallel corpora. Experimental evaluations across diverse datasets validate the effectiveness and scalability of our framework, demonstrating robust multilingual retention across languages. This work provides a principled pathway for adapting MCLIP to real-world scenarios, where computational efficiency and cross-lingual robustness are paramount. Our codes are available at https: //github. com/tiggers23/TaPCL-CiPCL.

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AAAI Conference 2025 Conference Paper

Enhancing Multi-Robot Semantic Navigation Through Multimodal Chain-of-Thought Score Collaboration

Zhixuan Shen
Haonan Luo
Kexun Chen
Fengmao Lv
Tianrui Li

Understanding how humans cooperatively utilize semantic knowledge to explore unfamiliar environments and decide on navigation directions is critical for house service multi-robot systems. Previous methods primarily focused on single-robot centralized planning strategies, which severely limited exploration efficiency. Recent research has considered decentralized planning strategies for multiple robots, assigning separate planning models to each robot, but these approaches often overlook communication costs. In this work, we propose Multimodal Chain-of-Thought Co-Navigation (MCoCoNav), a modular approach that utilizes multimodal Chain-of-Thought to plan collaborative semantic navigation for multiple robots. MCoCoNav combines visual perception with Vision Language Models (VLMs) to evaluate exploration value through probabilistic scoring, thus reducing time costs and achieving stable outputs. Additionally, a global semantic map is used as a communication bridge, minimizing communication overhead while integrating observational results. Guided by scores that reflect exploration trends, robots utilize this map to assess whether to explore new frontier points or revisit history nodes. Experiments on HM3D_v0.2 and MP3D demonstrate the effectiveness of our approach.

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IJCAI Conference 2025 Conference Paper

Enhancing Sampling Protocol for Point Cloud Classification Against Corruptions

Chongshou Li
Pin Tang
Tianrui Li
Yuheng Liu
Xinke Li

Established sampling protocols for 3D point cloud learning, such as Farthest Point Sampling (FPS) and Fixed Sample Size (FSS), have long been relied upon. However, real-world data often suffer from corruptions, such as sensor noise, which violates the benign data assumption in current protocols. As a result, these protocols are highly vulnerable to noise, posing significant safety risks in critical applications like autonomous driving. To address these issues, we propose an enhanced point cloud sampling protocol, PointSP, designed to improve robustness against point cloud corruptions. PointSP incorporates key point reweighting to mitigate outlier sensitivity and ensure the selection of representative points. It also introduces a local-global balanced downsampling strategy, which allows for scalable and adaptive sampling while maintaining geometric consistency. Additionally, a lightweight tangent plane interpolation method is used to preserve local geometry while enhancing the density of the point cloud. Unlike learning-based approaches that require additional model training, PointSP is architecture-agnostic, requiring no extra learning or modification to the network. This enables seamless integration into existing pipelines. Extensive experiments on synthetic and real-world corrupted datasets show that PointSP significantly improves the robustness and accuracy of point cloud classification, outperforming state-of-the-art methods across multiple benchmarks.

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AAAI Conference 2025 Conference Paper

Multi-Granularity Open Intent Classification via Adaptive Granular-Ball Decision Boundary

Yanhua Li
Xiaocao Ouyang
Chaofan Pan
Jie Zhang
Sen Zhao
Shuyin Xia
Xin Yang
Guoyin Wang

Open intent classification is critical for the development of dialogue systems, aiming to accurately classify known intents into their corresponding classes while identifying unknown intents. Prior boundary-based methods assumed known intents fit within compact spherical regions, focusing on coarse-grained representation and precise spherical decision boundaries. However, these assumptions are often violated in practical scenarios, making it difficult to distinguish known intent classes from unknowns using a single spherical boundary. To tackle these issues, we propose a Multi-granularity Open intent classification method via adaptive Granular-Ball decision boundary (MOGB). Our MOGB method consists of two modules: representation learning and decision boundary acquiring. To effectively represent the intent distribution, we design a hierarchical representation learning method. This involves iteratively alternating between adaptive granular-ball clustering and nearest sub-centroid classification to capture fine-grained semantic structures within known intent classes. Furthermore, multi-granularity decision boundaries are constructed for open intent classification by employing granular-balls with varying centroids and radii. Extensive experiments conducted on three public datasets demonstrate the effectiveness of our proposed method.

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IJCAI Conference 2025 Conference Paper

Non-collective Calibrating Strategy for Time Series Forecasting

Bin Wang
Yongqi Han
Minbo Ma
Tianrui Li
Junbo Zhang
Feng Hong
Yanwei Yu

Deep learning-based approaches have demonstrated significant advancements in time series forecasting. Despite these ongoing developments, the complex dynamics of time series make it challenging to establish the rule of thumb for designing the golden model architecture. In this study, we argue that refining existing advanced models through a universal calibrating strategy can deliver substantial benefits with minimal resource costs, as opposed to elaborating and training a new model from scratch. We first identify a multi-target learning conflict in the calibrating process, which arises when optimizing variables across time steps, leading to the underutilization of the model's learning capabilities. To address this issue, we propose an innovative calibrating strategy called Socket+Plug (SoP). This approach retains an exclusive optimizer and early-stopping monitor for each predicted target within each Plug while keeping the fully trained Socket backbone frozen. The model-agnostic nature of SoP allows it to directly calibrate the performance of any trained deep forecasting models, regardless of their specific architectures. Extensive experiments on various time series benchmarks and a spatio-temporal meteorological ERA5 dataset demonstrate the effectiveness of SoP, achieving up to a 22% improvement even when employing a simple MLP as the Plug (highlighted in Figure 1).

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EAAI Journal 2025 Journal Article

Non-convex regularized robust multimodal feature selection via self-representation learning for Alzheimer’s disease diagnosis

Xi Guo
Hongmei Chen
Biao Xiang
Zhong Yuan
Chuan Luo
Shi-Jinn Horng
Tianrui Li

Multimodal neuroimaging data fusion has become a key research direction in Alzheimer’s Disease (AD) diagnosis. However, existing methods face challenges such as (1) Limited robustness against outliers and noise, which hampers effective feature selection; (2) Limitations of conventional convex approximation methods, such as the ℓ 2, 1 norm, in approximating the ideal ℓ 2, 0 norm, making it challenging to capture sparse structures accurately; (3) Inadequate modeling of feature correlations, leading to missed identification of synergistic feature groups. To address these issues, this study proposes a Non-Convex Regularized Robust Multimodal Feature Selection method via Self-Representation Learning for Alzheimer’s Disease diagnosis (NCRRFS). Specifically, self-representation learning is employed to model the error terms of anomalous samples, enabling the adaptive detection and correction of abnormal data, thereby enhancing the robustness of the model. Furthermore, an ℓ 2, γ norm row sparsity constraint based on the Smoothly Clipped Absolute Deviation (SCAD) function is designed to more accurately approximate the ℓ 2, 0 norm. Additionally, a graph-structured regularization based on Pearson correlation promotes the selection of synergistic feature groups. Extensive experimental results demonstrate the effectiveness and superiority of the proposed method in the Alzheimer’s disease classification task.

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EAAI Journal 2025 Journal Article

Self-supervised contrastive learning for implicit collaborative filtering

Shipeng Song
Bin Liu
Fei Teng
Tianrui Li

Recommendation systems are a critical application of artificial intelligence (AI), driving personalized user experiences across various platforms. Recent advancements in contrastive learning-based recommendation algorithms have led to significant progress in self-supervised recommendation. A key method in this field is Bayesian Personalized Ranking (BPR), which has become a dominant approach for implicit collaborative filtering. However, the challenge of false-positive and false-negative examples in implicit feedback continues to hinder accurate preference learning. In this study, we introduce an efficient self-supervised contrastive learning framework that enhances the supervisory signal by incorporating positive feature augmentation and negative label augmentation. Our theoretical analysis reveals that this approach is equivalent to maximizing the likelihood estimation with latent variables representing user interest centers. Additionally, we present a novel negative label augmentation technique that selects unlabeled examples based on their relative ranking positions, enabling efficient augmentation with constant time complexity. Validation on the MovieLens-100k, MovieLens-1M, Yahoo! -R3, Yelp2018, and Gowalla datasets demonstrates that our method achieves over a 5% improvement in precision compared to the widely used BPR optimization objective, while maintaining comparable runtime efficiency.

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EAAI Journal 2025 Journal Article

Symmetric non-negative matrix factorization-based deep representation algorithm for multi-view clustering

Ping Deng
Xinying Zhou
Ji Xu
Wei Huang
Jie Wang
Dexian Wang
Tianrui Li

Symmetric Non-negative Matrix Factorization (SNMF) shows significant advantages in clustering task due to its unique mathematical properties. However, it still has several key limitations: (1) the single optimization scheme of traditional multiplicative update rule limits the flexibility of the algorithm; (2) linear factorization leads to insufficient representation ability for complex nonlinear features; (3) lack of learning rate guidance mechanism. These factors together constrain the algorithm representation learning ability in complex data. To address these issues, this paper proposes a SNMF-based Deep Representation algorithm for Multi-view Clustering (SNDRMvC). First, the matrix elements are decoupled, and the stochastic gradient descent as well as nonlinear activation function are used to implement non-negative matrix update. Then, based on the corresponding gradients of the elements and nonlinear function, the neural network learning mechanism is introduced into the SNMF update rule to construct a novel framework SNMF-based deep representation network for optimizing SNMF. This network aims to update the elements in the low-dimensional matrix of each view and fuse the low-dimensional matrices of multiple views to derive a consensus matrix. Finally, extensive experiments conducted on several public datasets demonstrate that the proposed algorithm exhibits notable advantages in clustering performance. We provide the code at: https: //github. com/Code706/SNDRMvC.

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AAAI Conference 2025 Conference Paper

TIME-FS: Joint Learning of Tensorial Incomplete Multi-View Unsupervised Feature Selection and Missing-View Imputation

Yanyong Huang
Minghui Lu
Wei Huang
Xiuwen Yi
Tianrui Li

Multi-view unsupervised feature selection (MUFS) has received considerable attention in recent years. Existing MUFS methods for processing unlabeled incomplete multi-view data, where some samples are missing in certain views, first impute the missing values and then perform feature selection on the completed dataset. However, treating imputation and feature selection as two separate processes overlooks their potential interactions. The graph-guided local structure gleaned from feature selection can aid in imputation, which in turn can enhance the feature selection performance. Additionally, most similarity graph-based MUFS methods suffer from high computational costs. To address these problems, we propose a novel MUFS method, termed Tensorial Incomplete Multi-view unsupErvised Feature Selection (TIME-FS). TIME-FS unifies missing value recovery, discriminative feature selection, and low-dimensional representation learning within a joint framework through matrix decomposition. Then, TIME-FS conducts CP decomposition on tensor data formed by the low-dimensional representations of different views to learn a consistent anchor graph across views and a view-preference weight matrix, both of which simultaneously guide missing view imputation and feature selection. Furthermore, an efficient algorithm with low time complexity and rapid convergence is proposed to solve TIME-FS. Extensive experimental results demonstrate the effectiveness and efficiency of TIME-FS over state-of-the-art methods.

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TIST Journal 2024 Journal Article

DNSRF: Deep Network-based Semi-NMF Representation Framework

Dexian Wang
Tianrui Li
Ping Deng
Zhipeng Luo
Pengfei Zhang
Keyu Liu
Wei Huang

Representation learning is an important topic in machine learning, pattern recognition, and data mining research. Among many representation learning approaches, semi-nonnegative matrix factorization (SNMF) is a frequently-used one. However, a typical problem of SNMF is that usually there is no learning rate guidance during the optimization process, which often leads to a poor representation ability. To overcome this limitation, we propose a very general representation learning framework (DNSRF) that is based on a deep neural net. Essentially, the parameters of the deep net used to construct the DNSRF algorithms are obtained by matrix element update. In combination with different activation functions, DNSRF can be implemented in various ways. In our experiments, we tested nine instances of our DNSRF framework on six benchmark datasets. In comparison with other state-of-the-art methods, the results demonstrate the superior performance of our framework, which is thus shown to have a great representation ability.

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AAAI Conference 2024 Conference Paper

Learning to Prompt Knowledge Transfer for Open-World Continual Learning

Yujie Li
Xin Yang
Hao Wang
Xiangkun Wang
Tianrui Li

This paper studies the problem of continual learning in an open-world scenario, referred to as Open-world Continual Learning (OwCL). OwCL is increasingly rising while it is highly challenging in two-fold: i) learning a sequence of tasks without forgetting knowns in the past, and ii) identifying unknowns (novel objects/classes) in the future. Existing OwCL methods suffer from the adaptability of task-aware boundaries between knowns and unknowns, and do not consider the mechanism of knowledge transfer. In this work, we propose Pro-KT, a novel prompt-enhanced knowledge transfer model for OwCL. Pro-KT includes two key components: (1) a prompt bank to encode and transfer both task-generic and task-specific knowledge, and (2) a task-aware open-set boundary to identify unknowns in the new tasks. Experimental results using two real-world datasets demonstrate that the proposed Pro-KT outperforms the state-of-the-art counterparts in both the detection of unknowns and the classification of knowns markedly. Code released at https://github.com/YujieLi42/Pro-KT.

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EAAI Journal 2024 Journal Article

LEFMIFS: Label enhancement and fuzzy mutual information for robust multilabel feature selection

Tengyu Yin
Hongmei Chen
Zhong Yuan
Binbin Sang
Shi-Jinn Horng
Tianrui Li
Chuan Luo

Feature selection is one of the quite important preprocessing steps in multilabel learning. However, multilabel feature selection is facing big challenges due to high-dimensional and noise-contaminated data. In addition, most existing multilabel feature selection methods mainly center around the assumption of uniform label distribution. Meanwhile, the difference in significance of the labels associated with the sample is ubiquitous in many real-world applications. Motivated by these, this paper proposes a robust multilabel feature selection algorithm in which label enhancement is embedded into feature selection. Firstly, based on exploring dual-space identifying information, the data distribution of natural neighbors is further discussed, and the original logical label is enhanced to the label distribution form. Furthermore, a robust multilabel β -precision fuzzy rough sets model (ML β PFRS) is constructed. Combining the algebraic view with the information view in ML β PFRS defines a novel multilabel fuzzy entropy, and some properties and relationships are discussed. What’s more, an objective evaluation function is designed to estimate the quality of the features. Finally, a label enhancement and fuzzy mutual information-based robust multilabel feature selection algorithm is proposed. The extensive experiments performed on thirteen publicly available datasets demonstrate that compared with some advanced algorithms, LEFMIFS is not only for obtaining the feature subset with better classification performance but also for maintaining good robustness.

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TIST Journal 2024 Journal Article

T-Distributed Stochastic Neighbor Embedding for Co-Representation Learning

Wei Chen
Hongjun Wang
Yinghui Zhang
Ping Deng
Zhipeng Luo
Tianrui Li

Co-clustering is the simultaneous clustering of the samples and attributes of a data matrix that provides deeper insight into data than traditional clustering. However, there is a lack of representation learning algorithms that serve this mechanism of co-clustering, and the current representation learning algorithms are limited to the sample perspective and lack the use of information in the attribute perspective. To solve this problem, in this article, ctSNE, a co-representation learning model based on t-distributed stochastic neighbor embedding, is proposed for unsupervised co-clustering, where ctSNE makes the dataset representation outputted more discriminative of row and column clusters (i.e. co-discrimination). On the basis of t-distributed stochastic neighbor embedding retaining the sample data distribution and local data structure, the philosophy of collaboration is introduced (i.e., row and column hidden relationship information) so that the ctSNE model is equipped with co-representation learning capability, which can effectively improve the performance of co-clustering. To prove the effectiveness of the ctSNE model, several classic co-clustering algorithms are used to check the co-representation performance of ctSNE, and a novel internal index based on an internal clustering index, known as total inertia, is proposed to demonstrate the effect of co-clustering. The numerous experimental results show that ctSNE has tremendous co-representation capability and can significantly improve the performance of co-clustering algorithms.

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IJCAI Conference 2024 Conference Paper

Unified View Imputation and Feature Selection Learning for Incomplete Multi-view Data

Yanyong Huang
Zongxin Shen
Tianrui Li
Fengmao Lv

Although multi-view unsupervised feature selection (MUFS) is an effective technology for reducing dimensionality in machine learning, existing methods cannot directly deal with incomplete multi-view data where some samples are missing in certain views. These methods should first apply predetermined values to impute missing data, then perform feature selection on the complete dataset. Separating imputation and feature selection processes fails to capitalize on the potential synergy where local structural information gleaned from feature selection could guide the imputation, thereby improving the feature selection performance in turn. Additionally, previous methods only focus on leveraging samples' local structure information, while ignoring the intrinsic locality of the feature space. To tackle these problems, a novel MUFS method, called UNified view Imputation and Feature selectIon lEaRning (UNIFIER), is proposed. UNIFIER explores the local structure of multi-view data by adaptively learning similarity-induced graphs from both the sample and feature spaces. Then, UNIFIER dynamically recovers the missing views, guided by the sample and feature similarity graphs during the feature selection procedure. Furthermore, the half-quadratic minimization technique is used to automatically weight different instances, alleviating the impact of outliers and unreliable restored data. Comprehensive experimental results demonstrate that UNIFIER outperforms other state-of-the-art methods.

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KER Journal 2022 Journal Article

A framework for belief revision under restrictions

Zhiguo Long
Hua Meng
Tianrui Li
Heng-Chao Li
Michael Sioutis

Abstract Traditional belief revision usually considers generic logic formulas, whilst in practical applications some formulas might even be inappropriate for beliefs. For instance, the formula $p \wedge q$ is syntactically consistent and is also an acceptable belief when there are no restrictions, but it might become unacceptable under restrictions in some context. If we assume that p represents ‘manufacturing product A ’ and q represents ‘manufacturing product B ’, an example of such a context would be the knowledge that there are not enough resources to manufacture them both and, hence, $p \wedge q$ would not be an acceptable belief. In this article, we propose a generic framework for belief revision under restrictions. We consider restrictions of either fixed or dynamic nature, and devise several postulates to characterize the behaviour of changing beliefs when new evidence emerges or the restriction changes. Moreover, we show that there is a representation theorem for each type of restriction. Finally, we discuss belief revision of qualitative spatio-temporal information under restrictions as an application of this new framework.

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TIST Journal 2022 Journal Article

Self-supervised Discriminative Representation Learning by Fuzzy Autoencoder

Wenlu Yang
Hongjun Wang
Yinghui Zhang
Zehao Liu
Tianrui Li

Representation learning based on autoencoders has received great concern for its potential ability to capture valuable latent information. Conventional autoencoders pursue minimal reconstruction error, but in most machine learning tasks such as classification and clustering, the discrimination of feature representation is also important. To address this limitation, an enhanced self-supervised discriminative fuzzy autoencoder (FAE) is innovatively proposed, which focuses on exploring information within data to guide the unsupervised training process and enhancing feature discrimination in a self-supervised manner. In FAE, fuzzy membership is applied to provide a means of self-supervised, which allows FAE can not only utilize AE’s outstanding representation learning capabilities but can also transform the original data into another space with improved discrimination. First, the objective function corresponding to FAE is proposed by reconstruction loss and clustering oriented loss simultaneously. Subsequently, Mini-Batch Gradient Descent is applied to infer the objective function and the detailed process is illustrated step by step. Finally, empirical studies on clustering tasks have demonstrated the superiority of FAE over the state of the art.

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TIST Journal 2019 Journal Article

Reconstruction of Hidden Representation for Robust Feature Extraction

Zeng Yu
Tianrui Li
Ning Yu
Yi Pan
Hongmei Chen
Bing Liu

This article aims to develop a new and robust approach to feature representation. Motivated by the success of Auto-Encoders, we first theoretically analyze and summarize the general properties of all algorithms that are based on traditional Auto-Encoders: (1) The reconstruction error of the input cannot be lower than a lower bound, which can be viewed as a guiding principle for reconstructing the input. Additionally, when the input is corrupted with noises, the reconstruction error of the corrupted input also cannot be lower than a lower bound. (2) The reconstruction of a hidden representation achieving its ideal situation is the necessary condition for the reconstruction of the input to reach the ideal state. (3) Minimizing the Frobenius norm of the Jacobian matrix of the hidden representation has a deficiency and may result in a much worse local optimum value. We believe that minimizing the reconstruction error of the hidden representation is more robust than minimizing the Frobenius norm of the Jacobian matrix of the hidden representation. Based on the above analysis, we propose a new model termed Double Denoising Auto-Encoders (DDAEs), which uses corruption and reconstruction on both the input and the hidden representation. We demonstrate that the proposed model is highly flexible and extensible and has a potentially better capability to learn invariant and robust feature representations. We also show that our model is more robust than Denoising Auto-Encoders (DAEs) for dealing with noises or inessential features. Furthermore, we detail how to train DDAEs with two different pretraining methods by optimizing the objective function in a combined and separate manner, respectively. Comparative experiments illustrate that the proposed model is significantly better for representation learning than the state-of-the-art models.

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AIJ Journal 2018 Journal Article

Predicting citywide crowd flows using deep spatio-temporal residual networks

Junbo Zhang
Yu Zheng
Dekang Qi
Ruiyuan Li
Xiuwen Yi
Tianrui Li

Forecasting the flow of crowds is of great importance to traffic management and public safety, and very challenging as it is affected by many complex factors, including spatial dependencies (nearby and distant), temporal dependencies (closeness, period, trend), and external conditions (e. g. weather and events). We propose a deep-learning-based approach, called ST-ResNet, to collectively forecast two types of crowd flows (i. e. inflow and outflow) in each and every region of a city. We design an end-to-end structure of ST-ResNet based on unique properties of spatio-temporal data. More specifically, we employ the residual neural network framework to model the temporal closeness, period, and trend properties of crowd traffic. For each property, we design a branch of residual convolutional units, each of which models the spatial properties of crowd traffic. ST-ResNet learns to dynamically aggregate the output of the three residual neural networks based on data, assigning different weights to different branches and regions. The aggregation is further combined with external factors, such as weather and day of the week, to predict the final traffic of crowds in each and every region. We have developed a real-time system based on Microsoft Azure Cloud, called UrbanFlow, providing the crowd flow monitoring and forecasting in Guiyang City of China. In addition, we present an extensive experimental evaluation using two types of crowd flows in Beijing and New York City (NYC), where ST-ResNet outperforms nine well-known baselines.

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IJCAI Conference 2016 Conference Paper

ST-MVL: Filling Missing Values in Geo-Sensory Time Series Data

Xiuwen Yi
Yu Zheng
Junbo Zhang
Tianrui Li

Many sensors have been deployed in the physical world, generating massive geo-tagged time series data. In reality, we usually lose readings of sensors at some unexpected moments because of sensor or communication errors. Those missing readings do not only affect real-time monitoring but also compromise the performance of further data analysis. In this paper, we propose a spatio-temporal multi-view-based learning (ST-MVL) method to collectively fill missing readings in a collection of geo-sensory time series data, considering 1) the temporal correlation between readings at different timestamps in the same series and 2) the spatial correlation between different time series. Our method combines empirical statistic models, consisting of Inverse Distance Weighting and Simple Exponential Smoothing, with data-driven algorithms, comprised of User-based and Item-based Collaborative Filtering. The former models handle the general missing cases based on empirical assumptions derived from history data over a long period, standing for two global views from a spatial and temporal perspective respectively. The latter algorithms deal with special cases where empirical assumptions may not hold, based on recent contexts of data, denoting two local views from a spatial and temporal perspective respectively. The predictions of the four views are aggregated to a final value in a multi-view learning algorithm. We evaluate our method based on Beijing air quality and meteorological data, finding our model's advantages beyond ten baseline approaches.

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EAAI Journal 2012 Journal Article

A new blockmodeling based hierarchical clustering algorithm for web social networks

Shaojie Qiao
Tianrui Li
Hong Li
Jing Peng
Hongmei Chen

Cluster analysis for web social networks becomes an important and challenging problem because of the rapid development of the Internet community like YouTube, Facebook and TravelBlog. To accurately partition web social networks, we propose a hierarchical clustering algorithm called HCUBE based on blockmodeling which is particularly suitable for clustering networks with complex link relations. HCUBE uses structural equivalence to compute the similarity among web pages and reduces a large and incoherent network into a set of smaller comprehensible subnetworks. HCUBE is actually a bottom-up agglomerative hierarchical clustering algorithm which uses the inter-connectivity and the closeness of clusters to group structurally equivalent pages in an effective fashion. In addition, we address the preliminaries of the proposed blockmodeling and the theoretical foundations of HCUBE clustering algorithm. In order to improve the efficiency of HCUBE, we optimize it by reducing its time complexity from O ( | V | 2 ) to O ( | V | 2 / p ), where p is a constant representing the number of initial partitions. Finally, we conduct experiments on real data and the results show that HCUBE is effective at partitioning web social networks compared to the Chameleon and k-means algorithms.

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