Non-negative Tensor Factorization Based on Alternating Large-scale Non-negativity-constrained Least Squares

Non-negative matrix factorization (NMF) and non-negative tensor factorization (NTF) have attracted much attention and have been successfully applied to numerous data analysis problems where the components of the data are necessarily non-negative such as chemical concentrations in experimental results or pixels in digital images. Especially, Andersson and Bro's PARAFAC algorithm with nonnegativity constraints (AB-PARAFAC-NC) provided the stateof-the-art NTF algorithm, which uses Bro and de Jong's nonnegativity-constrained least squares with single right hand side (NLS/S-RHS). However, solving an NLS with multiple right hand sides (NLS/M-RHS) problem by multiple NLS/SRHS problems is not recommended due to hidden redundant computation. In this paper, we propose an NTF algorithm based on alternating large-scale non-negativity-constrained least squares (NTF/ANLS) using NLS/M-RHS. In addition, we introduce an algorithm for the regularized NTF based on ANLS (RNTF/ANLS). Our experime...

• Type: Conference
• Year: 2007
• Where: BIBE
• Authors: Hyunsoo Kim, Haesun Park, Lars Eldén
• Keywords: BIBE 2007, Bioinformatics, Non-negative Tensor Factorization, NTF Algorithms, Stateof-the-art Ntf Algorithm