Object structure

Creator:

Zdunek, Rafał

Contributor:

Makowski, Ryszard - ed. ; Zarzycki, Jan - ed.

Title:

Regularized nonnegative matrix factorization: Geometrical interpretation and application to spectral unmixing

Subtitle:

.

Group publication title:

AMCS, Volume 24 (2014)

Subject and Keywords:

blind source separation ; nonnegative matrix factorization ; active-set algorithm ; regularized NMF ; polytope approximation

Abstract:

Nonnegative Matrix Factorization (NMF) is an important tool in data spectral analysis. However, when a mixing matrix or sources are not sufficiently sparse, NMF of an observation matrix is not unique. Many numerical optimization algorithms, which assure fast convergence for specific problems, may easily get stuck into unfavorable local minima of an objective function, resulting in very low performance. ; In this paper, we discuss the Tikhonov regularized version of the Fast Combinatorial NonNegative Least Squares (FC-NNLS) algorithm (proposed by Benthem and Keenan in 2004), where the regularization parameter starts from a large value and decreases gradually with iterations. A geometrical analysis and justification of this approach are presented. The numerical experiments, carried out for various benchmarks of spectral signals, demonstrate that this kind of regularization, when applied to the FC-NNLS algorithm, is essential to obtain good performance.

Publisher:

Zielona Góra: Uniwersytet Zielonogórski

Date:

2014

Resource Type:

artykuł

DOI:

10.2478/amcs-2014-0017

Pages:

233-247

Source:

AMCS, volume 24, number 2 (2014) ; click here to follow the link

Language:

eng

Rights:

Biblioteka Uniwersytetu Zielonogórskiego