Non-negative Multiple Tensor Factorization

Non-negative Tensor Factorization (NTF) is a widely used technique for decomposing a non-negative value tensor into sparse and reasonably interpretable factors. However, NTF performs poorly when the tensor is extremely sparse, which is often the case with real-world data and higher-order tensors. In...

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Bibliographic Details
Main Authors: Takeuchi, Koh, Tomioka, Ryota, Ishiguro, Katsuhiko, Kimura, Akisato, Sawada, Hiroshi
Format: Conference Proceeding
Language:English
Subjects:
Business
Conferences
Data mining
Drinking
Factorization
Geology
Machine Learning
Mathematical analysis
Matrix decomposition
Motion pictures
Non-negative Tensor Factorization
Online
Probabilistic logic
Shopping
Sparse matrices
Spatio-Temporal Pattern
Tensile stress
Tensors
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Summary:Non-negative Tensor Factorization (NTF) is a widely used technique for decomposing a non-negative value tensor into sparse and reasonably interpretable factors. However, NTF performs poorly when the tensor is extremely sparse, which is often the case with real-world data and higher-order tensors. In this paper, we propose Non-negative Multiple Tensor Factorization (NMTF), which factorizes the target tensor and auxiliary tensors simultaneously. Auxiliary data tensors compensate for the sparseness of the target data tensor. The factors of the auxiliary tensors also allow us to examine the target data from several different aspects. We experimentally confirm that NMTF performs better than NTF in terms of reconstructing the given data. Furthermore, we demonstrate that the proposed NMTF can successfully extract spatio-temporal patterns of people's daily life such as leisure, drinking, and shopping activity by analyzing several tensors extracted from online review data sets.
ISSN:1550-4786
2374-8486
DOI:10.1109/ICDM.2013.83