NDLI: Joint Tensor Factorization and Outlying Slab Suppression With Applications

Content Provider	IEEE Xplore Digital Library
Author	Xiao Fu Kejun Huang Wing-Kin Ma Sidiropoulos, N.D. Bro, R.
Copyright Year	1991
Abstract	We consider factoring low-rank tensors in the presence of outlying slabs. This problem is important in practice, because data collected in many real-world applications, such as speech, fluorescence, and some social network data, fit this paradigm. Prior work tackles this problem by iteratively selecting a fixed number of slabs and fitting, a procedure which may not converge. We formulate this problem from a group-sparsity promoting point of view, and propose an alternating optimization framework to handle the corresponding ℓ_p (0 <; p ≤ 1) minimization-based low-rank tensor factorization problem. The proposed algorithm features a similar per-iteration complexity as the plain trilinear alternating least squares (TALS) algorithm. Convergence of the proposed algorithm is also easy to analyze under the framework of alternating optimization and its variants. In addition, regularization and constraints can be easily incorporated to make use of a priori information on the latent loading factors. Simulations and real data experiments on blind speech separation, fluorescence data analysis, and social network mining are used to showcase the effectiveness of the proposed algorithm.
Sponsorship	IEEE Signal Processing Society
Starting Page	6315
Ending Page	6328
Page Count	14
File Size	4031233
File Format	PDF
ISSN	1053587X
Volume Number	63
Issue Number	23
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Slabs Tensile stress Signal processing algorithms Speech Optimization Algorithm design and analysis Robustness tensor decomposition Canonical polyadic decomposition group sparsity iteratively reweighted outliers PARAFAC robustness
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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