PARAFAC algorithms for large-scale problems

Authors:
Anh Huy Phan;Andrzej Cichocki
Affiliations:
Lab for Advanced Brain Signal Processing, BSI, RIKEN, Wakoshi, Saitama, Japan;Systems Research Institute Polish Academy of Science, Poland
Venue:
Neurocomputing
Year:
2011

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Abstract

Parallel factor analysis (PARAFAC) is a tensor (multiway array) factorization method which allows to find hidden factors (component matrices) from a multidimensional data. Most of the existing algorithms for the PARAFAC, especially the alternating least squares (ALS) algorithm need to compute Khatri-Rao products of tall factors and multiplication of large matrices, and due to this require high computational cost and large memory and are not suitable for very large-scale-problems. Hence, PARAFAC for large-scale data tensors is still a challenging problem. In this paper, we propose a new approach based on a modified ALS algorithm which computes Hadamard products, instead Khatri-Rao products, and employs relatively small matrices. The new algorithms are able to process extremely large-scale tensors with billions of entries. Extensive experiments confirm the validity and high performance of the developed algorithm in comparison with other well-known algorithms.