标题：Multifactor sparse feature extraction using Convolutive Nonnegative Tucker Decomposition
作者：Wu, Qiang; Zhang, Liqing; Cichocki, Andrzej
作者机构：[Wu, Qiang] Shandong Univ, Sch Informat Sci & Engn, Jinan, Shandong, Peoples R China.; [Zhang, Liqing] Shanghai Jiao Tong Univ, Dept Comp Sci & Engn 更多
会议名称：18th International Conference on Neural Information Processing (ICONIP)
会议日期：NOV 13-17, 2011
关键词：Nonnegative Tensor Decomposition; Convolutive Tucker Model; Alternating; Least Squares (ALS); Feature extraction; Robustness
摘要：Multilinear algebra of the higher-order tensor has been proposed as a potential mathematical framework for machine learning to investigate the relationships among multiple factors underlying the observations. One popular model Nonnegative Tucker Decomposition (NTD) allows us to explore the interactions of different factors with nonnegative constraints. In order to reduce degeneracy problem of tensor decomposition caused by component delays, convolutive tensor decomposition model is an appropriate model for exploring temporal correlations. In this paper, a flexible two stage algorithm for K-mode Convolutive Nonnegative Tucker Decomposition (K-CNTD) model is proposed using an alternating least square procedure. This model can be seen as a convolutive extension of Nonnegative Tucker Decomposition. The patterns across columns in convolutive tensor model are investigated to represent audio and image considering multiple factors. We employ the K-CNTD algorithm to extract the shift-invariant sparse features in different subspaces for robust speaker recognition and Alzheimer's Disease(AD) diagnosis task. The experimental results confirm the validity of our proposed algorithm and indicate that it is able to improve the speaker recognition performance especially in noisy conditions and has potential application on AD diagnosis. (C) 2013 Elsevier B.V. All rights reserved.