Linked PARAFAC/CP tensor decomposition and its fast implementation for multi-block tensor analysis

Authors:
Tatsuya Yokota;Andrzej Cichocki;Yukihiko Yamashita
Affiliations:
Tokyo Institute of Technology, Tokyo, Japan,RIKEN Brain Science Institute, Saitama, Japan;RIKEN Brain Science Institute, Saitama, Japan;Tokyo Institute of Technology, Tokyo, Japan
Venue:
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Year:
2012

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Abstract

In this paper we propose a new flexible group tensor analysis model called the linked CP tensor decomposition (LCPTD). The LCPTD method can decompose given multiple tensors into common factor matrices, individual factor matrices, and core tensors, simultaneously. We applied the Hierarchical Alternating Least Squares (HALS) algorithm to the LCPTD model; besides we impose additional constraints to obtain sparse and nonnegative factors. Furthermore, we conducted some experiments of this model to demonstrate its advantages over existing models.