Sparse non-negative tensor factorization using columnwise coordinate descent

Authors:
Ji Liu;Jun Liu;Peter Wonka;Jieping Ye
Affiliations:
Department of Computer Science and Engineering, Arizona State University, Tempe, AZ 85287, United States;Department of Computer Science and Engineering, Arizona State University, Tempe, AZ 85287, United States;Department of Computer Science and Engineering, Arizona State University, Tempe, AZ 85287, United States;Department of Computer Science and Engineering, Arizona State University, Tempe, AZ 85287, United States
Venue:
Pattern Recognition
Year:
2012

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Abstract

Many applications in computer vision, biomedical informatics, and graphics deal with data in the matrix or tensor form. Non-negative matrix and tensor factorization, which extract data-dependent non-negative basis functions, have been commonly applied for the analysis of such data for data compression, visualization, and detection of hidden information (factors). In this paper, we present a fast and flexible algorithm for sparse non-negative tensor factorization (SNTF) based on columnwise coordinate descent (CCD). Different from the traditional coordinate descent which updates one element at a time, CCD updates one column vector simultaneously. Our empirical results on higher-mode images, such as brain MRI images, gene expression images, and hyperspectral images show that the proposed algorithm is 1-2 orders of magnitude faster than several state-of-the-art algorithms.