Makowski, Ryszard - ed. ; Zarzycki, Jan - ed.
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.
Zielona Góra: Uniwersytet Zielonogórski
AMCS, volume 24, number 2 (2014) ; click here to follow the link
Biblioteka Uniwersytetu Zielonogórskiego
Apr 24, 2024
Apr 24, 2024
35
https://zbc.uz.zgora.pl/publication/88741
Edition name | Date |
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Regularized nonnegative matrix factorization: Geometrical interpretation and application to spectral unmixing | Apr 24, 2024 |
Siwek, Krzysztof Osowski, Stanisław Szupiluk, Ryszard Korbicz, Józef (1951- ) - ed.