标题:A Two Stage Algorithm for K-Mode Convolutive Nonnegative Tucker Decomposition
作者:Wu, Qiang; Zhang, Liqing; Cichocki, Andrzej
通讯作者:Wu, Q
作者机构:[Wu, Qiang] Shandong Univ, Sch Informat Sci & Engn, Jinan 250100, Shandong, Peoples R China.; [Zhang, Liqing] Shanghai Jiao Tong Univ, Dept Comp Sci 更多
会议名称:18th International Conference on Neural Information Processing (ICONIP)
会议日期:NOV 13-17, 2011
来源:NEURAL INFORMATION PROCESSING, PT II
出版年:2011
卷:7063
期:PART 2
页码:663-670
DOI:10.1007/978-3-642-24958-7_77
摘要:Higher order tensor model has been seen as a potential mathematical framework to manipulate the multiple factors underlying the observations. In this paper, we propose a flexible two stage algorithm for K-mode Convolutive Nonnegative Tucker Decomposition (K-CNTD) model by an alternating least square procedure. This model can be seen as a convolutive extension of Nonnegative Tucker Decomposition (NTD). Shift-invariant features in different; subspaces can be extracted by the K-CNTD algorithm. We impose additional sparseness constraint on the algorithm to find the part-based representations. Extensive simulation results indicate that the K-CNTD algorithm is efficient and provides good performance for a feature extraction task.
收录类别:CPCI-S;EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-81855218278&doi=10.1007%2f978-3-642-24958-7_77&partnerID=40&md5=9667040f1e073b72359fec6822b4f16b
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