Nonnegative Tucker Decomposition

Nonnegative tensor factorization (NTF) is a recent multiway (multilinear) extension of nonnegative matrix factorization (NMF), where nonnegativity constraints are imposed on the CANDECOMP/PARAFAC model. In this paper we consider the Tucker model with nonnegativity constraints and develop a new tensor factorization method, referred to as nonnegative Tucker decomposition (NTD). The main contributions of this paper include: (1) multiplicative updating algorithms for NTD; (2) an initialization method for speeding up convergence; (3) a sparseness control method in tensor factorization. Through several computer vision examples, we show the useful behavior of the NTD, over existing NTF and NMF methods.

• Type: Conference
• Year: 2007
• Where: CVPR
• Authors: Yong-Deok Kim, Seungjin Choi
• Keywords: Computer Vision, CVPR 2007, Nonnegative Matrix Factorization, Nonnegative Tensor Factorization, Nonnegative Tucker Decomposition, Sparseness Control Method, Tensor Factorization Method