Jianwen Zhang

JBHI Journal 2023 Journal Article

Remote Respiratory and Cardiac Motion Patterns Separation With 4D Imaging Radars

• Zhi Li
• Tian Jin
• Xikun Hu
• Yongkun Song
• Jianwen Zhang
• Zhenqun Sang

Radar-based noncontact physiological signals monitoring is meaningful for daily health monitoring, post-disaster rescue, and public security. This paper focuses on the theoretical and experimental study of noncontact respiratory and cardiac motion signals separation by remote sensing using a four-dimensional (4D) imaging radar. To adaptively separate respiratory and cardiac motion patterns, we propose a variational mode separation (VMS) algorithm. VMS is established on optimizing a variational problem to separate different modes. It minimizes the energy overlap of the heartbeat and respiration signals as well as their harmonics with an equality constraint. Both simulation and real scene data results show that the proposed VMS algorithm is suitable for separating the weaker cardiac motion pattern from the strong respiratory motion pattern, restraining the influence of respiration harmonics on the heartbeat component. Furthermore, we have implemented continuous remote monitoring of respiratory rate (RR) and heart rate (HR) by employing the proposed method in a real scene. The results validate the consistency with the reference respiration belt and electrocardiogram (ECG). The root mean square errors (RMSEs) of RR and HR for the remote measurement are 0. 13 breaths per minute (brpm) and 1. 7 beats per minute (bpm), respectively.

EAAI Journal 2017 Journal Article

Fast source term estimation using the PGA-NM hybrid method

• Hui Li
• Jianwen Zhang

There are significant challenges related to estimating the source term of the atmospheric release. Urged on by robots in performing emergency responding tasks, a fast and accurate algorithm for this inversion problem is indispensable. Sometimes the NM simplex algorithm is efficient in the optimization problem, but sometimes the quality of convergence is unacceptable as a numerical breakdown, even for smooth and well-behaved functions. In contrast, full convergence might be seen in parallel genetic algorithms with a comparative slower convergence. In this paper we combine the PGA and the NM simplex algorithm by initializing simplex from the final individual of PGA results and obtaining the best vertex through simplex algorithm thereafter. A numerical simulation of the proposed algorithm shows noteworthy improvement of efficiency and robustness, compared with the PGA or the NM algorithm only.

AAAI Conference 2015 Conference Paper

Maximin Separation Probability Clustering

• Gao Huang
• Jianwen Zhang
• Shiji Song
• Zheng Chen

This paper proposes a new approach for discriminative clustering. The intuition is, for a good clustering, one should be able to learn a classifier from the clustering labels with high generalization accuracy. Thus we define a novel metric to evaluate the quality of a clustering labeling, named Minimum Separation Probability (MSP), which is a lower bound of the generalization accuracy of a classifier learnt from the clustering labeling. We take MSP as the objective to maximize and propose our approach Maximin Separation Probability Clustering (MSPC), which has several attractive properties, such as invariance to anisotropic feature scaling and intuitive probabilistic explanation for clustering quality. We present three efficient optimization strategies for MSPC, and analyze their interesting connections to existing clustering approaches, such as maximum margin clustering (MMC) and discriminative k-means. Empirical results on real world data sets verify that MSP is a robust and effective clustering quality measure. It is also shown that the proposed algorithms compare favorably to state-of-the-art clustering algorithms in both accuracy and efficiency.

AAAI Conference 2014 Conference Paper

Knowledge Graph Embedding by Translating on Hyperplanes

• Zhen Wang
• Jianwen Zhang
• Jianlin Feng
• Zheng Chen

We deal with embedding a large scale knowledge graph composed of entities and relations into a continuous vector space. TransE is a promising method proposed recently, which is very efficient while achieving state-of-the-art predictive performance. We discuss some mapping properties of relations which should be considered in embedding, such as reflexive, one-to-many, many-to-one, and many-to-many. We note that TransE does not do well in dealing with these properties. Some complex models are capable of preserving these mapping properties but sacrifice efficiency in the process. To make a good trade-off between model capacity and efficiency, in this paper we propose TransH which models a relation as a hyperplane together with a translation operation on it. In this way, we can well preserve the above mapping properties of relations with almost the same model complexity of TransE. Additionally, as a practical knowledge graph is often far from completed, how to construct negative examples to reduce false negative labels in training is very important. Utilizing the one-to-many/many-to-one mapping property of a relation, we propose a simple trick to reduce the possibility of false negative labeling. We conduct extensive experiments on link prediction, triplet classification and fact extraction on benchmark datasets like WordNet and Freebase. Experiments show TransH delivers significant improvements over TransE on predictive accuracy with comparable capability to scale up.

AAAI Conference 2013 Conference Paper

A Hierarchical Aspect-Sentiment Model for Online Reviews

• Suin Kim
• Jianwen Zhang
• Zheng Chen
• Alice Oh
• Shixia Liu

To help users quickly understand the major opinions from massive online reviews, it is important to automatically reveal the latent structure of the aspects, sentiment polarities, and the association between them. However, there is little work available to do this effectively. In this paper, we propose a hierarchical aspect sentiment model (HASM) to discover a hierarchical structure of aspect-based sentiments from unlabeled online reviews. In HASM, the whole structure is a tree. Each node itself is a two-level tree, whose root represents an aspect and the children represent the sentiment polarities associated with it. Each aspect or sentiment polarity is modeled as a distribution of words. To automatically extract both the structure and parameters of the tree, we use a Bayesian nonparametric model, recursive Chinese Restaurant Process (rCRP), as the prior and jointly infer the aspect-sentiment tree from the review texts. Experiments on two real datasets show that our model is comparable to two other hierarchical topic models in terms of quantitative measures of topic trees. It is also shown that our model achieves better sentence-level classification accuracy than previously proposed aspect-sentiment joint models.

AAAI Conference 2010 Conference Paper

Multitask Bregman Clustering

• Jianwen Zhang
• Changshui Zhang

Traditional clustering methods deal with a single clustering task on a single data set. However, in some newly emerging applications, multiple similar clustering tasks are involved simultaneously. In this case, we not only desire a partition for each task, but also want to discover the relationship among clusters of different tasks. It’s also expected that the learnt relationship among tasks can improve performance of each single task. In this paper, we propose a general framework for this problem and further suggest a specific approach. In our approach, we alternatively update clusters and learn relationship between clusters of different tasks, and the two phases boost each other. Our approach is based on the general Bregman divergence, hence it’s suitable for a large family of assumptions on data distributions and divergences. Empirical results on several benchmark data sets validate the approach.

AAAI Conference 2010 Conference Paper

What if the Irresponsible Teachers Are Dominating?

• Shuo Chen
• Jianwen Zhang
• Guangyun Chen
• Changshui Zhang

As the Internet-based crowdsourcing services become more and more popular, learning from multiple teachers or sources has received more attention of the researchers in the machine learning area. In this setting, the learning system is dealing with samples and labels provided by multiple teachers, who in common cases, are non-expert. Their labeling styles and behaviors are usually diverse, some of which are even detrimental to the learning system. Thus, simply putting them together and utilizing the algorithms designed for singleteacher scenario would be not only improper, but also damaging. The problem calls for more specific methods. Our work focuses on a case where the teachers are composed of good ones and irresponsible ones. By irresponsible, we mean the teacher who takes the labeling task not seriously and label the sample at random without inspecting the sample itself. This behavior is quite common when the task is not attractive enough and the teacher just wants to finish it as soon as possible. Sometimes, the irresponsible teachers could take a considerable part among all the teachers. If we do not take out their effects, our learning system would be ruined with no doubt. In this paper, we propose a method for picking out the good teachers with promising experimental results. It works even when the irresponsible teachers are dominating in numbers.

IJCAI Conference 2009 Conference Paper

• Jianwen Zhang
• Yangqiu Song
• Gang Chen
• Changshui Zhang

This paper deals with evolutionary clustering, which refers to the problem of clustering data with distribution drifting along time. Starting from a density estimation view to clustering problems, we propose two general on-line frameworks. In the first framework, i. e. , historical data dependent (HDD), current model distribution is designed to approximate both current and historical data distributions. In the second framework, i. e. , historical model dependent (HMD), current model distribution is designed to approximate both current data distribution and historical model distribution. Both frameworks are based on the general exponential family mixture (EFM) model. As a result, all conventional clustering algorithms based on EFMs can be extended to evolutionary setting under the two frameworks. Empirical results validate the two frameworks.