NDLI: Fast nonnegative tensor factorization for very large-scale problems using two-stage procedure

Content Provider	IEEE Xplore Digital Library
Author	Anh Huy Phan Cichocki, A.
Copyright Year	2009
Description	Author affiliation: Lab for Advanced Brain Signal Processing, Brain Science Institute - RIKEN, Wako-shi, Saitama 351-0198, JAPAN (Anh Huy Phan; Cichocki, A.)
Abstract	Parallel factor analysis (PARAFAC) is a multi-way decomposition method which allows to find hidden factors from the raw tensor data. Recently, the nonnegative tensor factorization (NTF), a variant of the model with nonnegativity constraints imposed on hidden factors has attracted interesting due to meaningful representation with many potential applications in neuroscience, bioinformatics, chemometrics etc [1], [2]. NTF algorithms can be easily extended from algorithms for nonnegative matrix factorization (NMF) by forming learning rules on the unfolding tensor [1], [3]. However, they often compute Khatri-Rao products of factors which lead to large matrices, and require large memory for temporal variables. Hence decomposition of large-scale tensor is still a challenging problem for NTF. PARAFAC by alternating least squares (ALS) can explain the raw tensor by a small number of rank-one tensor with a high fitness. Based on this advantage, we propose a new fast NTF algorithm which factorizes the approximate tensor obtained from the PARAFAC. Our new algorithm computes Hadamard products, therefore it is extremely fast in comparison with all the existing NTF algorithms. Extensive experiments confirm the validity, high performance and high speed of the developed algorithm.
Starting Page	297
Ending Page	300
File Size	756269
Page Count	4
File Format	PDF
ISBN	9781424451791
DOI	10.1109/CAMSAP.2009.5413274
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-13
Publisher Place	Aruba
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Tensile stress Signal processing algorithms Adaptive signal processing Cost function Large-scale systems Computational efficiency Matrix decomposition Least squares approximation Least squares methods Biomedical signal processing
Content Type	Text
Resource Type	Article

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