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Seminar abstract

Sparse Coding and an Lipschitz Auxiliary Function Algorithm

Chris Ding
Professor
University of Texas at Arlington


Abstract :

Sparse coding enforces solutions of pattern recognition methods to contain mostly zeros and a small number of nonzero elements. This has several important applications. In feature selection, this enforces entire row of the regression coefficients to be zero, such eliminates this data/feature dimension. This L2,1-norm based approach is becoming a popular feature selection method. In compressed sensing, an input signal (an image or example) is encoded with s very small number of dictionary signals. This leads to an improved presentation of the input signal, comparing to traditional orthogonal basis methods. In this talk, we will briefly explain these sparse coding methods. In addition, we introduce a new solution algorithm which uses the auxiliary function approach popularly used in nonnegative matrix factorization and a Lipschitz continuity condition. This algorithm can efficiently solve L1, L2,1, L0 norm based sparse coding problems.

This talk is based on a paper entitled: Towards Structural Sparsity: An explicit L2/L0 Approach, by Dijun Luo, Chris Ding, Heng Huang. This paper wons the best-paper-runner-up (2nd best paper) award in ICDM 2010, the leading international conference on data mining.

Bio:

Dr. Chris Ding earned a Ph.D. from Columbia University. He did research at California Institute of Technology, Jet Propulsion Laboratory and Lawrence Berkeley National Laboratory, before joining University of Texas at Arlington as a tenured professor in 2007. His research areas are machine mining, bioinformatics, high performance computing, focusing on matrix tensor approaches. He has served on program committees of NIPS, ICML, KDD, ICDM, SDM, AAAI conferences. He was funding proposal reviewer for National Science Foundations of US, Israel, Ireland and Hong Kong. He has given invited seminars at University of California at Berkeley, Stanford University, Carnegie Mellon University, University of Waterloo, University of Alberta, Google Research, IBM Research, Hong Kong University, National University of Singapore, Beijing University and Tsinghua University. He published 160 research papers that have been cited 5200 times.
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