Zhang Yi

AAAI Conference 2026 Conference Paper

Class Incremental Medical Image Segmentation via Prototype-Guided Calibration and Dual-Aligned Distillation

• Shengqian Zhu
• Chengrong Yu
• Qiang Wang
• Ying Song
• Guangjun Li
• Jiafei Wu
• Xiaogang Xu
• Zhang Yi

Class incremental medical image segmentation (CIMIS) aims to preserve knowledge of previously learned classes while learning new ones without relying on old-class annotations. However, existing methods 1) either adopt one-size-fits-all strategies that treat all spatial regions and feature channels equally, which may hinder the preservation of accurate old knowledge, 2) or focus solely on aligning local prototypes with global ones for old classes while overlooking their local representations in new data, leading to knowledge degradation. To mitigate the above issues, we propose Prototype-Guided Calibration Distillation (PGCD) and Dual-Aligned Prototype Distillation (DAPD) for CIMIS in this paper. Specifically, PGCD exploits prototype-to-feature similarity to calibrate class-specific distillation intensity in different spatial regions, effectively reinforcing reliable old knowledge and suppressing misleading cues from old classes. Complementarily, DAPD aligns the local prototypes of old classes extracted from the current model with both global historical prototypes and local prototypes, further enhancing segmentation performance on old categories. Comprehensive evaluations on two widely used multi-organ segmentation benchmarks demonstrate that our method outperforms current state-of-the-art methods, highlighting its robustness and generalization capabilities.

AAAI Conference 2026 Conference Paper

CNM-UNet: Continuous Ordinary Differential Equations for Medical Image Segmentation

• Tianqi Xu
• Yashi Zhu
• Quansong He
• Yue Cao
• Kaishen Wang
• Zhang Yi
• Tao He

Integrating Ordinary Differential Equations (ODEs) with U-shaped neural networks has emerged as a novel direction in medical image segmentation. Current networks predominantly employ discretization methods incorporating ODEs. However, these methods face inherent trade-offs between model compactness, computational accuracy, and efficiency. Continuous ODE solutions were rarely studied because they face three limitations: high computational costs, long training time, and poor generalization ability. To address these limitations, we propose an innovative Continuous Neural Memory ODE UNet (CNM-UNet), which replaces all hierarchical decoder layers in vanilla UNet with a single Continuous Neural Memory ODEs Block (CNM-Block) decoder, significantly reducing computation costs and improving training efficiency. CNM-UNet leverages ODEs' dynamic properties to establish continuous temporal feature extraction. For alleviating the generalization problem, a DUal SElf-updated (DUSE) strategy based on test-time adaptation principles is introduced to enhance cross-domain generalization. Experimental results demonstrate CNM-UNet's comprehensive advantages in computational capacity, convergence speed, and cross-domain adaptability, offering new insights for practical deployment of continuous ODE methodologies for medical image segmentation.

AAAI Conference 2026 Conference Paper

GeoCoBox: Box-supervised 3D Tumor Segmentation via Geometric Co-embedding

• Tianzhong Lan
• Zhang Yi
• Xiuyuan Xu
• Min Zhu

Data economics drives AI by optimizing data usage, reducing costs, and enhancing efficiency. In 3D tumor segmentation, efficiency is crucial due to the high demand for labor-intensive manual annotations. Box-supervised segmentation offers a promising alternative but is constrained by tumor morphology complexity and boundary ambiguity. In this paper, we propose a novel 3D tumor segmentation model that integrates both positional and embedding features to facilitate inter-task collaboration. We introduce an Anatomical-Driven Class Activation Map to predefine the complex tumor morphology prior, which is further refined by our Geometric Pixel Co-embedding Learner. This learner utilizes contrastive learning to encode semantic information between center and edge pixels, enhancing pixel clustering and progressively refining tumor boundary segmentation in a coarse-to-fine manner. Our approach outperforms existing box-supervised methods in segmentation performance, with extensive experiments on four tumor datasets demonstrating significant improvements. This work provides a cost-effective and efficient solution for tumor segmentation, advancing the application of data economics in medical imaging.

AAAI Conference 2026 Conference Paper

Mitigating Entity Hallucinations in 3D Radiology Report Generation via Dual-Stream Alignment

• Lingyu Zhou
• Yue Yu
• Zhang Yi
• Xiuyuan Xu

Entity hallucination poses a major challenge in radiology report generation (RRG), particularly for 3D CT scans where complex spatial contexts amplify factual errors. To address this, medical entity phrases serve as key carriers for multi-modal prompting, integrating expert knowledge into the vision-language model. Current methods use unified cross-attention for volume-phrase alignment, failing to account for anatomical specificity during the alignment process. In this work, we introduce the Dual-stream Entity Alignment Reporting network (DEAR) that separately models organ and lesion entities to resolve anatomical bias. Specifically, the dual-stream entity aligner is designed to partition medical entity phrases into organ and lesion streams, feeding them into separate cross-attention blocks in parallel to achieve fine-grained volume–phrase alignment. For structurally regular and spatially stable organ entities, an organ-guided cross-attention (OGCA) block is proposed to enforce structural consistency by retrieving the top-k voxel tokens via volume–phrase similarity and preserving spatial connectivity through morphological dilation. Meanwhile, a lesion-guided cross-attention (LGCA) block is introduced for structurally irregular and spatially variable lesion entities, enhancing anomaly sensitivity through phrase-weighted attention and refining discriminative boundaries via 3D residual Laplacian filtering. Experiments demonstrate that DEAR significantly reduces entity hallucinations and improves clinical factuality in 3D RRG benchmarks.

JBHI Journal 2026 Journal Article

Rethinking Propagation Methods for Interactive Medical Image Segmentation

• Shengqian Zhu
• Yuncheng Shen
• Yingyong Yin
• Ying Song
• Zhang Yi
• Guangjun Li
• Junjie Hu

Propagation-based methods have drawn increasing research attention in interactive medical image segmentation. However, existing propagation-based methods face two significant challenges: 1) Due tothe continuous nature of anatomical structures within the organs and tumors throughout the volume, over-propagation is likely to occur as the propagation process reaches the end of structures, leadingto a degradation in segmentation performance. 2) During the multi-round refinement process, selecting the worst-segmented slice for refinement tends to hinder the optimization of segmentation results. To overcome these challenges, we propose the Discrepancy Aware Network (DANet), which includes a Discrepancy Learning Module (DLM) and employs a confidence loss to achieve accurate segmentation. Specifically, DLM captures the temporal-contextual discrepancy between previous and current slices, enabling the model to perceive the variations of the target. Furthermore, the confidence loss is responsible for regularizing the over-confident segmentation at the image level by estimating the target foreground. Additionally, we design a straightforward slice selection strategy to optimize the refinement process. Extensive experimental results on five public medical datasets demonstrate significant improvements over state-of-the-art methods (e. g. , with +1. 07% improvement on the MSD-Spleen dataset).

AAAI Conference 2026 Conference Paper

RoSE: A Role Correlation Structure-Enhanced Model for Multi-Event Argument Extraction

• Geting Huang
• Jilong Zhang
• Kai Zhou
• Zhang Yi
• Xiuyuan Xu

Event co-occurrences have been proven effective for event argument extraction (EAE) in previous studies; however, few have considered intra- and inter-event role correlations. Since role varies among different event types, event structure heterogeneity and overlap pose significant challenges to EAE. To address this issue, we propose a Role Correlation Structure-Enhanced model for Multi-Event Argument Extraction (RoSE), capable of capturing both heterogeneity and overlap of event structures through modeling role correlations. The proposed RoSE model employs a joint context-prompts input, role-centric graph-guided encoder (RoGE), and role-specific information fusion (RoIF). The RoGE is designed to enhance the intra- and inter-event role correlation between prompts and their corresponding event contexts. The RoIF module utilizes intra-event role information to improve multi-event arguments extraction. Extensive experiments on four widely-used benchmarks (RAMS, WikiEvents, MLEE, and ACE05) demonstrate that our proposed approach achieves state-of-the-art performance, validating the effectiveness of incorporating both intra- and inter-event role correlations.

YNIMG Journal 2025 Journal Article

Distinguishing the roles of edge, color, and other surface information in basic and superordinate scene representation

• Liansheng Yao
• Qiufang Fu
• Chang Hong Liu
• Jianyong Wang
• Zhang Yi

NeurIPS Conference 2025 Conference Paper

MobileODE: An Extra Lightweight Network

• Le Yu
• Jun Wu
• Bo Gou
• Xiangde Min
• Lei Zhang
• Zhang Yi
• Tao He

Depthwise-separable convolution has emerged as a significant milestone in the lightweight development of Convolutional Neural Networks (CNNs) over the past decade. This technique consists of two key components: depthwise convolution, which captures spatial information, and pointwise convolution, which enhances channel interactions. In this paper, we propose a novel method to lightweight CNNs through the discretization of Ordinary Differential Equations (ODEs). Specifically, we optimize depthwise-separable convolution by replacing the pointwise convolution with a discrete ODE module, termed the \emph{\textbf{C}hannelwise \textbf{O}DE \textbf{S}olver (COS)}. The COS module is constructed by a simple yet efficient direct differentiation Euler algorithm, using learnable increment parameters. This replacement reduces parameters by over $98. 36$\% compared to conventional pointwise convolution. By integrating COS into MobileNet, we develop a new extra lightweight network called MobileODE. With carefully designed basic and inverse residual blocks, the resulting MobileODEV1 and MobileODEV2 reduce channel interaction parameters by $71. 0$\% and $69. 2$\%, respectively, compared to MobileNetV1, while achieving higher accuracy across various tasks, including image classification, object detection, and semantic segmentation. The code is available at {\url{https: //github. com/cashily/MobileODE}}.

NeurIPS Conference 2025 Conference Paper

On Reasoning Strength Planning in Large Reasoning Models

• Leheng Sheng
• An Zhang
• Zijian Wu
• Weixiang Zhao
• Changshuo Shen
• Zhang Yi
• Xiang Wang
• Tat-Seng Chua

Recent studies empirically reveal that large reasoning models (LRMs) can automatically allocate more reasoning strengths (\ie the number of reasoning tokens) for harder problems, exhibiting difficulty-awareness for better task performance. While this automatic reasoning strength allocation phenomenon has been widely observed, its underlying mechanism remains largely unexplored. To this end, we provide explanations for this phenomenon from the perspective of model activations. \textbf{We find evidence that LRMs pre-plan the reasoning strengths in their activations even before generation, with this reasoning strength causally controlled by the magnitude of a pre-allocated directional vector. } Specifically, we show that the number of reasoning tokens is predictable solely based on the question activations using linear probes, indicating that LRMs estimate the required reasoning strength in advance. We then uncover that LRMs encode this reasoning strength through a pre-allocated directional vector embedded in the activations of the model, where the vector’s magnitude modulates the reasoning strength. Subtracting this vector can lead to reduced reasoning token number and performance, while adding this vector can lead to increased reasoning token number and even improved performance. We further reveal that this direction vector consistently yields positive reasoning length prediction, and it modifies the logits of end-of-reasoning token \texttt{} to affect the reasoning length. Finally, we demonstrate two potential applications of our findings: overthinking behavior detection and enabling efficient reasoning on simple problems. Our work provides new insights into the internal mechanisms of reasoning in LRMs and offers practical tools for controlling their reasoning behaviors. Our code is available at \url{https: //anonymous. 4open. science/r/LRM-plans-CoT-7E04}.

IJCAI Conference 2024 Conference Paper

A Lightweight U-like Network Utilizing Neural Memory Ordinary Differential Equations for Slimming the Decoder

• Quansong He
• Xiaojun Yao
• Jun Wu
• Zhang Yi
• Tao He

In recent years, advanced U-like networks have demonstrated remarkable performance in medical image segmentation tasks. However, their drawbacks, including excessive parameters, high computational complexity, and slow inference speed, pose challenges for practical implementation in scenarios with limited computational resources. Existing lightweight U-like networks have alleviated some problems, but they often have pre-designed structures and consist of non-detachable modules, limiting their application scenarios. In this paper, we propose three plug-and-play decoders by employing different discretization methods of the neural memory Ordinary Differential Equation (nmODE). These decoders integrate features at various levels of abstraction by processing information from skip connections and performing numerical operations on upward paths. Through experiments on the PH2, ISIC2017, and ISIC2018 datasets, we embed these decoders into different U-like networks, demonstrating their effectiveness in significantly reducing the number of parameters and computation while maintaining performance. In summary, the proposed discretized nmODE decoder is capable of reducing the number of parameters by about 20% ~ 50% and computation by up to 74%, while being adaptive to all U-like networks. Our code is available at https: //github. com/nayutayuki/Lightweight-nmODE-Decoders-For-U-like-networks.

JBHI Journal 2024 Journal Article

Adaptive Annotation Correlation Based Multi-Annotation Learning for Calibrated Medical Image Segmentation

• Wei Huang
• Lei Zhang
• Xin Shu
• Zizhou Wang
• Zhang Yi

Medical image segmentation is a fundamental task in many clinical applications, yet current automated segmentation methods rely heavily on manual annotations, which are inherently subjective and prone to annotation bias. Recently, modeling annotator preference has garnered great interest, and several methods have been proposed in the past two years. However, the existing methods completely ignore the potential correlation between annotations, such as complementary and discriminative information. In this work, the A daptive annotation C orrela T ion based mult I -ann O tation Lear N ing ( ACTION ) method is proposed for calibrated medical image segmentation. ACTION employs consensus feature learning and dynamic adaptive weighting to leverage complementary information across annotations and emphasize discriminative information within each annotation based on their correlations, respectively. Meanwhile, memory accumulation-replay is proposed to accumulate the prior knowledge and integrate it into the model to enable the model to accommodate the multi-annotation setting. Two medical image benchmarks with different modalities are utilized to evaluate the performance of ACTION, and extensive experimental results demonstrate that it achieves superior performance compared to several state-of-the-art methods.

JBHI Journal 2024 Journal Article

ICNoduleNet: Enhancing Pulmonary Nodule Detection Performance on Sharp Kernel CT Imaging

• Tianzhong Lan
• Fanxin Zeng
• Zhang Yi
• Xiuyuan Xu
• Min Zhu

Thoracic computed tomography (CT) currently plays the primary role in pulmonary nodule detection, where the reconstruction kernel significantly impacts performance in computer-aided pulmonary nodule detectors. The issue of kernel selection affecting performance has been overlooked in pulmonary nodule detection. This paper first introduces a novel pulmonary nodule detection dataset named Reconstruction Kernel Imaging for Pulmonary Nodule Detection (RKPN) for quantifying algorithm differences between the two imaging types. The dataset contains pairs of images taken from the same patient on the same date, featuring both smooth (B31f) and sharp kernel (B60f) reconstructions. All other imaging parameters and pulmonary nodule labels remain entirely consistent across these pairs. Extensive quantification reveals mainstream detectors perform better on smooth kernel imaging than on sharp kernel imaging. To address suboptimal detection on the sharp kernel imaging, we further propose an image conversion-based pulmonary nodule detector called ICNoduleNet. A lightweight 3D slice-channel converter (LSCC) module is introduced to convert sharp kernel images into smooth kernel images, which can sufficiently learn inter-slice and inter-channel feature information while avoiding introducing excessive parameters. We conduct thorough experiments that validate the effectiveness of ICNoduleNet, it takes sharp kernel images as input and can achieve comparable or even superior detection performance to the baseline that uses the smooth kernel images. The evaluation shows promising results and proves the effectiveness of ICNoduleNet.

IJCAI Conference 2024 Conference Paper

Strengthening Layer Interaction via Dynamic Layer Attention

• Kaishen Wang
• Xun Xia
• Jian Liu
• Zhang Yi
• Tao He

In recent years, employing layer attention to enhance interaction among hierarchical layers has proven to be a significant advancement in building network structures. In this paper, we delve into the distinction between layer attention and the general attention mechanism, noting that existing layer attention methods achieve layer interaction on fixed feature maps in a static manner. These static layer attention methods limit the ability for context feature extraction among layers. To restore the dynamic context representation capability of the attention mechanism, we propose a Dynamic Layer Attention (DLA) architecture. The DLA comprises dual paths, where the forward path utilizes an improved recurrent neural network block, named Dynamic Sharing Unit (DSU), for context feature extraction. The backward path updates features using these shared context representations. Finally, the attention mechanism is applied to these dynamically refreshed feature maps among layers. Experimental results demonstrate the effectiveness of the proposed DLA architecture, outperforming other state-of-the-art methods in image recognition and object detection tasks. Additionally, the DSU block has been evaluated as an efficient plugin in the proposed DLA architecture. The code is available at https: //github. com/tunantu/Dynamic-Layer-attention.

JBHI Journal 2022 Journal Article

Deep Neural Network With Structural Similarity Difference and Orientation-Based Loss for Position Error Classification in the Radiotherapy of Graves’ Ophthalmopathy Patients

• Wenjie Liu
• Lei Zhang
• Guyu Dai
• Xiangbin Zhang
• Guangjun Li
• Zhang Yi

Identifying position errors for Graves’ ophthalmopathy (GO) patients using electronic portal imaging device (EPID) transmission fluence maps is helpful in monitoring treatment. However, most of the existing models only extract features from dose difference maps computed from EPID images, which do not fully characterize all information of the positional errors. In addition, the position error has a three-dimensional spatial nature, which has never been explored in previous work. To address the above problems, a deep neural network (DNN) model with structural similarity difference and orientation-based loss is proposed in this paper, which consists of a feature extraction network and a feature enhancement network. To capture more information, three types of Structural SIMilarity (SSIM) sub-index maps are computed to enhance the luminance, contrast, and structural features of EPID images, respectively. These maps and the dose difference maps are fed into different networks to extract radiomic features. To acquire spatial features of the position errors, an orientation-based loss function is proposed for optimal training. It makes the data distribution more consistent with the realistic 3D space by integrating the error deviations of the predicted values in the left-right, superior-inferior, anterior-posterior directions. Experimental results on a constructed dataset demonstrate the effectiveness of the proposed model, compared with other related models and existing state-of-the-art methods.

JBHI Journal 2021 Journal Article

DeepUWF: An Automated Ultra-Wide-Field Fundus Screening System via Deep Learning

• Wei Zhang
• Xiujuan Zhao
• Yuanyuan Chen
• Jie Zhong
• Zhang Yi

The emerging ultra-wide field of view (UWF) fundus color imaging is a powerful tool for fundus screening. However, manual screening is labor-intensive and subjective. Based on 2644 UWF images, a set of early fundus abnormal screening system named DeepUWF is developed. DeepUWF includes an abnormal fundus screening subsystem and a disease diagnosis subsystem for three kinds of fundus diseases (retinal tear & retinal detachment, diabetic retinopathy and pathological myopia). The components in the system are composed of a set of excellent convolutional neural networks and two custom classifiers. However, the contrast of UWF images used in the research is low, which seriously limits the extraction of fine features of UWF images by depth model. Therefore, the high specificity and low sensitivity of prediction results have always been difficult problems in research. In order to solve this problem, six kinds of image preprocessing techniques are adopted, and their effects on the prediction performance of fundus abnormal and three kinds of fundus diseases models are studied. A variety of experimental indicators are used to evaluate the algorithms for validity and reliability. The experimental results show that these preprocessing methods are helpful to improve the learning ability of the networks and achieve good sensitivity and specificity. Without ophthalmologists, DeepUWF has potential application value, which is helpful for fundus health screening and workflow improvement.

JBHI Journal 2020 Journal Article

MediMLP: Using Grad-CAM to Extract Crucial Variables for Lung Cancer Postoperative Complication Prediction

• Tao He
• Jixiang Guo
• Nan Chen
• Xiuyuan Xu
• Zihuai Wang
• Kaiyu Fu
• Lunxu Liu
• Zhang Yi

Lung cancer postoperative complication prediction (PCP) is significant for decreasing the perioperative mortality rate after lung cancer surgery. In this paper we concentrate on two PCP tasks: (1) the binary classification for predicting whether a patient will have postoperative complications; and (2) the three-class multi-label classification for predicting which postoperative complication a patient will experience. Furthermore, an important clinical requirement of PCP is the extraction of crucial variables from electronic medical records. We propose a novel multi-layer perceptron (MLP) model called medical MLP (MediMLP) together with the gradient-weighted class activation mapping (Grad-CAM) algorithm for lung cancer PCP. The proposed MediMLP, which involves one locally connected layer and fully connected layers with a shortcut connection, simultaneously extracts crucial variables and performs PCP tasks. The experimental results indicated that MediMLP outperformed normal MLP on two PCP tasks and had comparable performance with existing feature selection methods. Using MediMLP and further experimental analysis, we found that the variable of “time of indwelling drainage tube” was very relevant to lung cancer postoperative complications.

TIST Journal 2019 Journal Article

A Local Mean Representation-based K -Nearest Neighbor Classifier

• Jianping Gou
• Wenmo Qiu
• Zhang Yi
• Yong Xu
• Qirong Mao
• Yongzhao Zhan

K -nearest neighbor classification method (KNN), as one of the top 10 algorithms in data mining, is a very simple and yet effective nonparametric technique for pattern recognition. However, due to the selective sensitiveness of the neighborhood size k, the simple majority vote, and the conventional metric measure, the KNN-based classification performance can be easily degraded, especially in the small training sample size cases. In this article, to further improve the classification performance and overcome the main issues in the KNN-based classification, we propose a local mean representation-based k -nearest neighbor classifier (LMRKNN). In the LMRKNN, the categorical k -nearest neighbors of a query sample are first chosen to calculate the corresponding categorical k -local mean vectors, and then the query sample is represented by the linear combination of the categorical k -local mean vectors; finally, the class-specific representation-based distances between the query sample and the categorical k -local mean vectors are adopted to determine the class of the query sample. Extensive experiments on many UCI and KEEL datasets and three popular face databases are carried out by comparing LMRKNN to the state-of-art KNN-based methods. The experimental results demonstrate that the proposed LMRKNN outperforms the related competitive KNN-based methods with more robustness and effectiveness.

AAAI Conference 2017 Conference Paper

Cascade Subspace Clustering

• Xi Peng
• Jiashi Feng
• Jiwen Lu
• Wei-Yun Yau
• Zhang Yi

In this paper, we recast the subspace clustering as a veri- fication problem. Our idea comes from an assumption that the distribution between a given sample x and cluster centers Ω is invariant to different distance metrics on the manifold, where each distribution is defined as a probability map (i. e. soft-assignment) between x and Ω. To verify this socalled invariance of distribution, we propose a deep learning based subspace clustering method which simultaneously learns a compact representation using a neural network and a clustering assignment by minimizing the discrepancy between pair-wise sample-centers distributions. To the best of our knowledge, this is the first work to reformulate clustering as a verification problem. Moreover, the proposed method is also one of the first several cascade clustering models which jointly learn representation and clustering in end-to-end manner. Extensive experimental results show the effectiveness of our algorithm comparing with 11 state-of-the-art clustering approaches on four data sets regarding to four evaluation metrics.

IJCAI Conference 2016 Conference Paper

Deep Subspace Clustering with Sparsity Prior

• Xi Peng
• Shijie Xiao
• Jiashi Feng
• Wei-Yun Yau
• Zhang Yi

Subspace clustering aims to cluster unlabeled samples into multiple groups by implicitly seeking a subspace to fit each group. Most of existing methods are based on a shallow linear model, which may fail in handling data with nonlinear structure. In this paper, we propose a novel subspace clustering method - deeP subspAce clusteRing with sparsiTY prior (PARTY) - based on a new deep learning architecture. PARTY explicitly learns to progressively transform input data into nonlinear latent space and to be adaptive to the local and global subspace structure simultaneously. In particular, considering local structure, PARTY learns representation for the input data with minimal reconstruction error. Moreover, PARTY incorporates a prior sparsity information into the hidden representation learning to preserve the sparse reconstruction relation over the whole data set. To the best of our knowledge, PARTY is the first deep learning based subspace clustering method. Extensive experiments verify the effectiveness of our method.

AAAI Conference 2015 Conference Paper

Robust Subspace Clustering via Thresholding Ridge Regression

• Xi Peng
• Zhang Yi
• Huajin Tang

Given a data set from a union of multiple linear subspaces, a robust subspace clustering algorithm fits each group of data points with a low-dimensional subspace and then clusters these data even though they are grossly corrupted or sampled from the union of dependent subspaces. Under the framework of spectral clustering, recent works using sparse representation, low rank representation and their extensions achieve robust clustering results by formulating the errors (e. g. , corruptions) into their objective functions so that the errors can be removed from the inputs. However, these approaches have suffered from the limitation that the structure of the errors should be known as the prior knowledge. In this paper, we present a new method of robust subspace clustering by eliminating the effect of the errors from the projection space (representation) rather than from the input space. We firstly prove that `1-, `2-, and `∞-norm-based linear projection spaces share the property of intra-subspace projection dominance, i. e. , the coefficients over intra-subspace data points are larger than those over inter-subspace data points. Based on this property, we propose a robust and efficient subspace clustering algorithm, called Thresholding Ridge Regression (TRR). TRR calculates the `2-norm-based coefficients of a given data set and performs a hard thresholding operator; and then the coefficients are used to build a similarity graph for clustering. Experimental studies show that TRR outperforms the state-of-the-art methods with respect to clustering quality, robustness, and time-saving.

AAAI Conference 2014 Conference Paper

A Local Non-Negative Pursuit Method for Intrinsic Manifold Structure Preservation

• DongDong Chen
• Jian Cheng Lv
• Zhang Yi

The local neighborhood selection plays a crucial role for most representation based manifold learning algorithms. This paper reveals that an improper selection of neighborhood for learning representation will introduce negative components in the learnt representations. Importantly, the representations with negative components will affect the intrinsic manifold structure preservation. In this paper, a local non-negative pursuit (LNP) method is proposed for neighborhood selection and non-negative representations are learnt. Moreover, it is proved that the learnt representations are sparse and convex. Theoretical analysis and experimental results show that the proposed method achieves or outperforms the state-of-the-art results on various manifold learning problems.

IJCAI Conference 2013 Conference Paper

Manifold Alignment Based on Sparse Local Structures of More Corresponding Pairs

• Xiaojie Li
• Jian Cheng Lv
• Zhang Yi

Manifold alignment is to extract the shared latent semantic structure from multiple manifolds. The joint adjacency matrix plays a key role in manifold alignment. To construct the matrix, it is crucial to get more corresponding pairs. This paper proposes an approach to obtain more and reliable corresponding pairs in terms of local structure correspondence. The sparse reconstruction weight matrix of each manifold is established to preserve the local geometry of the original data set. The sparse correspondence matrices are constructed using the sparse local structures of corresponding pairs across manifolds. Further more, a new energy function for manifold alignment is proposed to simultaneously match the corresponding instances and preserve the local geometry of each manifold. The shared low dimensional embedding, which provides better descriptions for the intrinsic geometry and relations between different manifolds, can be obtained by solving the optimization problem with closed-form solution. Experiments demonstrate the effectiveness of the proposed algorithm.

TCS Journal 2010 Journal Article

Adaptive multiple minor directions extraction in parallel using a PCA neural network

• Kok Kiong Tan
• Jian Cheng Lv
• Zhang Yi
• Sunan Huang

TCS Journal 2007 Journal Article

Convergence analysis of the OJAn MCA learning algorithm by the deterministic discrete time method

• Dezhong Peng
• Zhang Yi

TCS Journal 2006 Journal Article

Global convergence of Oja's PCA learning algorithm with a non-zero-approaching adaptive learning rate

• Jian Cheng Lv
• Zhang Yi
• K.K. Tan

TCS Journal 2004 Journal Article

Global exponential convergence of recurrent neural networks with variable delays

• Zhang Yi