Bi-Objective Nonnegative Matrix Factorization: Linear Versus Kernel-Based Models
Abstract
Nonnegative matrix factorization (NMF) is a powerful class of feature extraction techniques that has been successfully applied in many fields, namely in signal and image processing. Current NMF techniques have been limited to a single-objective problem in either its linear or nonlinear kernel-based formulation. In this paper, we propose to revisit the NMF as a multi-objective problem, in particular a bi-objective one, where the objective functions defined in both input and feature spaces are taken into account. By taking the advantage of the sum-weighted method from the literature of multi-objective optimization, the proposed bi-objective NMF determines a set of nondominated, Pareto optimal, solutions instead of a single optimal decomposition. Moreover, the corresponding Pareto front is studied and approximated. Experimental results on unmixing real hyperspectral images confirm the efficiency of the proposed bi-objective NMF compared with the state-of-the-art methods.
Cite
@article{arxiv.1501.05684,
title = {Bi-Objective Nonnegative Matrix Factorization: Linear Versus Kernel-Based Models},
author = {Paul Honeine and Fei Zhu},
journal= {arXiv preprint arXiv:1501.05684},
year = {2015}
}