Biography
Prof. Chris H.Q. Ding
Prof. Chris H.Q. Ding
University of Texas, USA
Title: L21 norm and Trace Norm: Sparse Coding and Low-rank Matrix Models for Data Recoveryand Feature Selection
Abstract: 
Sparse coding and low rank models are recently developed techniques in machine learning,, widely used for robust data recovery (recover severely corrupted images) and feature selection (select genes responsible a disease) . Sparse coding uses L21 norm based multi-class feature selection. Trace norm regularization recently emerged as the popular form to enforce low rank in data representation.Robust data recovery uses L1 or L21 normsas error functions. Dictionary learning obtainsdata representations better than PCA by learning the sparse codes. Many new ideas and variants have been proposed. We survey these new and growing areas.
Biography: 
Chris Ding is a professor of computer science at University of Texas at Arlington. He obtained Ph.D. at Columbia University, and worked at Caltech, Jet Propulsion Lab and Berkeley National Lab, before joining UTA.His research results were published as front cover of Science and praised by Nature commentary. His research includes machine learning , high performance computing, bioinformatics, etc. He made original contributions to PCA and K-means clustering, nonnegative matrix factorizations, and spectralclustering . In 2006 he proposed L21norm now widely used in machine learning. He has given seminars in UC Berkeley, Stanford, CMU, Waterloo U, Alberta U, Google Research, IBM Research, Microsoft Research, etc. He has published about 200 papers that were cited 39740 times.