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ICASSP
2011
IEEE

Novel hierarchical ALS algorithm for nonnegative tensor factorization

10 years 9 months ago
Novel hierarchical ALS algorithm for nonnegative tensor factorization
The multiplicative algorithms are well-known for nonnegative matrix and tensor factorizations. The ALS algorithm for canonical decomposition (CP) has been proved as a “workhorse” algorithm for general multiway data. Unfortunately, for CP with nonnegativity constraints, this algorithm with a rectifier (projection) may not converge to the desired solution without additional regularization parameters in matrix inverses. The hierarchical ALS algorithm improves the performance of the ALS algorithm, outperforms the multiplicative algorithm. However, NTF algorithms can face problem with collinear or bias data. In this paper, we propose a novel algorithm which overwhelmingly outperforms all the multiplicative, and (H)ALS algorithms. By solving the nonnegative quadratic programming problems, a general algorithm of the HALS has been derived and experimentally confirmed its validity and high performance for normal and difficult benchmarks, and for real-world EEG dataset.
Anh Huy Phan, Andrzej Cichocki, Kiyotoshi Matsuoka
Added 21 Aug 2011
Updated 21 Aug 2011
Type Journal
Year 2011
Where ICASSP
Authors Anh Huy Phan, Andrzej Cichocki, Kiyotoshi Matsuoka, Jianting Cao
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