- Tytuł:
- Regularized nonnegative matrix factorization: Geometrical interpretation and application to spectral unmixing
- Autorzy:
- Zdunek, R.
- Powiązania:
- https://bibliotekanauki.pl/articles/329732.pdf
- Data publikacji:
- 2014
- Wydawca:
- Uniwersytet Zielonogórski. Oficyna Wydawnicza
- Tematy:
-
blind source separation
nonnegative matrix factorization
active set algorithm
regularized NMF
polytope approximation
ślepa separacja sygnału
nieujemna faktoryzacja macierzy - Opis:
- 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.
- Źródło:
-
International Journal of Applied Mathematics and Computer Science; 2014, 24, 2; 233-247
1641-876X
2083-8492 - Pojawia się w:
- International Journal of Applied Mathematics and Computer Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł