학술논문
Non-Negative Tensor Factorization using Alpha and Beta Divergences
Document Type
Conference
Source
2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07 Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on. 3:III-1393-III-1396 Apr, 2007
Subject
Language
ISSN
1520-6149
2379-190X
2379-190X
Abstract
In this paper we propose new algorithms for 3D tensor decomposition/factorization with many potential applications, especially in multi-way Blind Source Separation (BSS), multidimensional data analysis, and sparse signal/image representations. We derive and compare three classes of algorithms: Multiplicative, Fixed-Point Alternating Least Squares (FPALS) and Alternating Interior-Point Gradient (AIPG) algorithms. Some of the proposed algorithms are characterized by improved robustness, efficiency and convergence rates and can be applied for various distributions of data and additive noise.