Paul S. Pang, Auckland University of Technology, New Zealand Title: Learning Linear Discriminant Analysis in Real World Applications

Abstract: Linear Discriminant Analysis (LDA) has been widely researched to implement various computation intelligences, such as pattern recognition, data mining, bioinformatics and robotics. However, learning LDA in real world confronts difficulties in different application scenarios. Starting from classic LDA, this talk introduces a series of recent LDA developments, where an LDA model is enabled to be learned either in one batch session, or incrementally by ILDA through instance-space merging, or through LDA eigenspace merging; In multi-agent background, LDA can be learned cooperatively by a number of agents with knowledge sharing in-between each other; In a special case, a created LDA can be renovated by LDA splitting with a minimum processing on the raw data instance; Even In some physical limited environment such as the remote space, LDA can be actively learned by an independent agent on fewer selected curiosity instances, or by a multiple of agents in a competitive and cooperative learning manner.

Bio Sketch: Dr. Paul S. Pang is the director of center for adaptive pattern recognition systems, Knowledge Engineering and Discovery research Institute (KEDRI), Auckland University of Technology, New Zealand. His research interests include SVM aggregating intelligence, incremental & multi-task learning, Bioinformatics, and adaptive soft computing for industrial applications. He has been serving as a program member and session chair for several international conferences including ISNN, ICONIP, ICNNSP, IJCNN, and WCCI. He was a best paper winner of IEEE ICNNSP 2003, IEEE DMAI2008, and an invited speaker of ICONIP07/BrainIT 2007. He is acting as a guest editor of Journal of Memetic Computing, Springer, and a regular paper reviewer for a number of refereed international journals including IEEE Trans on NN, TKDE, SMC-B. Dr. Pang is a Senior Member of IEEE, and a Member of IEICE, and ACM.