Sciweavers

Share
ICANNGA
2007
Springer

Novel Multi-layer Non-negative Tensor Factorization with Sparsity Constraints

11 years 5 months ago
Novel Multi-layer Non-negative Tensor Factorization with Sparsity Constraints
In this paper we present a new method of 3D non-negative tensor factorization (NTF) that is robust in the presence of noise and has many potential applications, including multi-way blind source separation (BSS), multi-sensory or multi-dimensional data analysis, and sparse image coding. We consider alpha- and beta-divergences as error (cost) functions and derive three different algorithms: (1) multiplicative updating; (2) fixed point alternating least squares (FPALS); (3) alternating interior-point gradient (AIPG) algorithm. We also incorporate these algorithms into multilayer networks. Experimental results confirm the very useful behavior of our multilayer 3D NTF algorithms with multi-start initializations. 1 Models and Problem Formulation Tensors (also known as n-way arrays or multidimensional arrays) are used in a variety of applications ranging from neuroscience and psychometrics to chemometrics [1–4]. Nonnegative matrix factorization (NMF), Non-negative tensor factorization (N...
Andrzej Cichocki, Rafal Zdunek, Seungjin Choi, Rob
Added 08 Jun 2010
Updated 08 Jun 2010
Type Conference
Year 2007
Where ICANNGA
Authors Andrzej Cichocki, Rafal Zdunek, Seungjin Choi, Robert J. Plemmons, Shun-ichi Amari
Comments (0)
books