讲座题目： Ensemble learning: a multi-classifier framework for machine learning
讲座人： ALAN WEE-CHUNG LIEW
报告内容：Many problems can be formulated as recovering a lowranktensor.Although an increasingly common task, tensor recovery remains a challenging problem because of the delicacy associated with the decomposition of higher order tensors. To overcome these difficulties, existing approaches often proceed by unfolding tensors into matrices and then apply techniques for matrix completion. We show here that such matricization fails to exploit the tensor structure and may lead to suboptimal procedure. More specically, we investigate a convex optimization approach to tensor completion by directly minimizing a tensor nuclear norm and prove that this leads to an improved sample size requirement. To establish our results, we develop a series of algebraic and probabilistic techniques such as characterization of subdieretial for tensor nuclear norm and concentration inequalities for tensor martingales, which may be of independent interests and could be useful in other tensor related problems. This is joint work with Ming Yuan.
报告人简历：Dr. ALAN WEE-CHUNG LIEW is currently an Associate Professor with the School of Information & Communication Technology, Griffith University, Australia. His research interest is in the fields of medical imaging, computer vision, machine learning, pattern recognition, and bioinformatics.
He has published extensively in these areas and is the author of one book and more than 150 book chapters, journals and conference papers, and holds two international patents. He has engaged actively in professional activities such as on the technical program committee and technical chair of many conferences, on several journal editorial boards (including IEEE Transactions on Fuzzy Systems), as assessor for Australian Research Council, HK Research Grant Council, and Austria Research Grant Council, and as reviewer for many international conferences and journals. He is a senior member of IEEE.