Abstract: Mining various dependence structures from data are important to many data mining
applications. In this talk, an overview is made on tasks of mining various independent bases spanned subspace among high dimension data, mining hidden autoregressive structure and hidden Markov structure from temporal sequences, and mining mixture and topological structures from samples of multiple objects data. Then, efforts towards a key challenge, namely making learning on a finite size of samples with model selection ability, have been discussed in two typical streams. Bayesian Ying Yang system provides a unified framework for summarizing these dependence structures, and BYY harmony learning provides a promising tool for solving this key challenge, with new mechanisms for model selection and regularization. Moreover, BYY harmony learning is further justified from both an information theoretic perspective and a generalized projection geometry.

Lei Xu is a chair professor of Computer Sci & Eng., Chinese Univ Hong Kong. He completed his Ph.D thesis at Tsinghua Univ. by the end of 1986, then joined Dept.Math, Peking Univ in 1987 first as a postdoc and then was exceptionally promoted to associate professor in 1988. During 1989-93, he worked at several universities in Finland, Canada and USA, including Harvard and MIT. He joined CUHK in 1993 as senior lecturer, became professor in 1996 and took the current chair professor in 2002. Prof. Xu has served or is serving as associate editor for several international journals, including Neural Networks, IEEE Trans. on Neural Networks, as a governor of International Neural Network Society (01-03), the chair of Computational Finance Technical Committee of IEEE Neural Networks Society (01-03), and a past president of Asian-Pacific Neural Networks Assembly. Prof. Xu has been internationally known with his several well-cited contributions on adaptive PCA and independence learning, classifier combination and mixture model based learning, rival penalized competition and topological self-organization, as well as his Bayesian Ying-Yang unified statistical learning system and theory. Also, he and Oja’s invention on Randomized Hough Transform has a wide impact in the field of pattern recognition. He has given over 40 keynote/plenary/invited/tutorial talks in international major neural networks conferences, including ICONIP, WCNN, IEEE-ICNN, IJCNN, etc. He served as a program committee chair of ICONIP’96, ICANN-ICONIP03, a general chair of IDEAL’98, IDEAL’00, IEEE CIFER’03. He has received several Chinese national prestigious academic awards (including 1994 Chinese National Nature Science Award) and international awards (including 1995 INNS Leadership Award). Prof Xu is an IEEE Fellow and a Fellow of International Association for Pattern Recognition, and a member of European Academy of Sciences.