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20160907-30 桂杰:Research of Theories and Methods of Pattern Classification and ...

2016-9-4 22:22| 发布者: 程一-计算所| 查看: 6569| 评论: 0

摘要: 报告嘉宾:桂杰(中科院合肥智能机械研究所)报告时间:2016年09月07日(星期三)晚20:00(北京时间)报告题目: Research of Theories and Methods of Pattern Classification and Dimensionality Reduction主持人 ...

报告嘉宾:桂杰(中科院合肥智能机械研究所)

报告时间:2016年09月07日(星期三)晚20:00(北京时间)

报告题目: Research of Theories and Methods of Pattern Classification and Dimensionality Reduction

主持人:王楠楠(西安电子科技大学)


报告摘要:

In this webinar, we will introduce some progresses in classification and dimensionality reduction: 

1. Classification:A novel pattern classification framework, namely, representative vector machines (or RVMs for short) are proposed in [1]. The basic idea of RVMs is to assign the class label of a test example according to its nearest representative vector. The proposed RVMs establish a unified framework of classical classifiers because the nearest neighbor (NN) classifier, the nearest feature line (NFL) classifier, the nearest feature plane (NFP) classifier, the nearest centroid (NC) classifier, nearest feature space (NFS) classifier, support vector machine (SVM), and sparse representation-based classification (SRC) can be interpreted as the special cases of RVMs with different definitions of representative vectors.

2. Dimensionality reduction: feature selection. A comprehensive survey of feature selection based on structured sparsity is given in [2]. This paper [2] is a survey on sparse representation with its applications in feature selection.

3. Dimensionality reduction: feature extraction [3]. How to automatically estimate the optimal regularization parameter of spectral regression discriminant analysis (SRDA) is proposed in [3].


参考文献:

[1]. Jie Gui, Tongliang Liu, Dacheng Tao, Zhenan Sun, Tieniu Tan, "Representative Vector Machines: A unified framework for classical classifiers", IEEE Transactions on Cybernetics, vol. 46, no. 8, pp. 1877-1888, 2016.

[2]. Jie Gui, Zhenan Sun, Shuiwang Ji, DachengTao, Tieniu Tan, "Feature Selection Based on Structured Sparsity: A Comprehensive Study", IEEE Transactions on Neural Networks and Learning Systems,DOI:10.1109/TNNLS.2016.2551724.

[3]. Jie Gui, Zhenan Sun, Shuiwang Ji, Jun Cheng and Xindong Wu, "How to estimate the regularization parameter for spectral regression discriminant analysis and its kernel version?", IEEE Transactions on Circuits and Systems for Video Technology, vol. 24, no. 2, pp. 211-223, 2014.


报告人简介:

Jie Gui is an Associate Professor in Institute of Intelligent Machines, Chinese Academy of Sciences. He obtained his PhD degree in pattern recognition and intelligent systems from the University of Science and Technology of China, in 2010. He has worked as a postdoc research fellow in the National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences. He has obtained the 2012 Endeavour Australia Cheung Kong Research Fellowship and worked in University of Technology, Sydney. His current research interests include machine learning, pattern recognition, data mining, image processing, and computer vision.

He has published more than 30 papers in international journals and conferences such as IEEE TNNLS, IEEE TCYB, IEEE TIP, IEEE TCSVT, IEEE TSMCS and Pattern recognition (PR). He has obtained several honors and awards, such as Second Prize of Anhui Province Best Paper Award of Science, 2016, Endeavour Australia Cheung Kong Research Fellowships, 2012 and Guanghua Outstanding Graduate Research Award of the University of Science and Technology of China, 2009. 

He is currently a Senior Member of IEEE. He serves as the peer reviewer of many leading journals such as IEEE TNNLS, IEEE TCYB, IEEE TIP, IEEE TIFS, IEEE SMCS, PR, and ACM TKDD. He is the PC Member of many top conferences such as 2015 International Joint Conference on Artificial Intelligence (IJCAI'15), 2016 SIAM International Conference on Data Mining (SDM '16) and 2015 SIAM International Conference on Data Mining (SDM'15).


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