Extended sparse nonnegative matrix factorization

Kurt Stadlthanner; Fabian J. Theis; Carlos G. Puntonet; Elmar W. Lang

doi:10.1007/11494669_31

Extended sparse nonnegative matrix factorization

Kurt Stadlthanner, Fabian J. Theis, Carlos G. Puntonet, Elmar W. Lang

Research output: Contribution to journal › Conference article › peer-review

10 Scopus citations

Abstract

In sparse nonnegative component analysis (sparse NMF) a given dataset is decomposed into a mixing matrix and a feature data set, which are both nonnegative and fulfill certain sparsity constraints. In this paper, we extend the sparse NMF algorithm to allow for varying sparsity in each feature and discuss the uniqueness of an involved projection step. Furthermore, the eligibility of the extended sparse NMF algorithm for blind source separation is investigated.

Original language	English
Pages (from-to)	249-256
Number of pages	8
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3512
DOIs	https://doi.org/10.1007/11494669_31
State	Published - 2005
Externally published	Yes
Event	8th International Workshop on Artificial Neural Networks, IWANN 2005: Computational Intelligence and Bioinspired Systems - Vilanova i la Geltru, Spain Duration: 8 Jun 2005 → 10 Jun 2005

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title = "Extended sparse nonnegative matrix factorization",

abstract = "In sparse nonnegative component analysis (sparse NMF) a given dataset is decomposed into a mixing matrix and a feature data set, which are both nonnegative and fulfill certain sparsity constraints. In this paper, we extend the sparse NMF algorithm to allow for varying sparsity in each feature and discuss the uniqueness of an involved projection step. Furthermore, the eligibility of the extended sparse NMF algorithm for blind source separation is investigated.",

author = "Kurt Stadlthanner and Theis, {Fabian J.} and Puntonet, {Carlos G.} and Lang, {Elmar W.}",

year = "2005",

doi = "10.1007/11494669_31",

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journal = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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note = "8th International Workshop on Artificial Neural Networks, IWANN 2005: Computational Intelligence and Bioinspired Systems ; Conference date: 08-06-2005 Through 10-06-2005",

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AU - Stadlthanner, Kurt

AU - Theis, Fabian J.

AU - Puntonet, Carlos G.

AU - Lang, Elmar W.

PY - 2005

Y1 - 2005

N2 - In sparse nonnegative component analysis (sparse NMF) a given dataset is decomposed into a mixing matrix and a feature data set, which are both nonnegative and fulfill certain sparsity constraints. In this paper, we extend the sparse NMF algorithm to allow for varying sparsity in each feature and discuss the uniqueness of an involved projection step. Furthermore, the eligibility of the extended sparse NMF algorithm for blind source separation is investigated.

AB - In sparse nonnegative component analysis (sparse NMF) a given dataset is decomposed into a mixing matrix and a feature data set, which are both nonnegative and fulfill certain sparsity constraints. In this paper, we extend the sparse NMF algorithm to allow for varying sparsity in each feature and discuss the uniqueness of an involved projection step. Furthermore, the eligibility of the extended sparse NMF algorithm for blind source separation is investigated.

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DO - 10.1007/11494669_31

M3 - Conference article

AN - SCOPUS:25144449073

SN - 0302-9743

VL - 3512

SP - 249

EP - 256

JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

T2 - 8th International Workshop on Artificial Neural Networks, IWANN 2005: Computational Intelligence and Bioinspired Systems

Y2 - 8 June 2005 through 10 June 2005

ER -

Extended sparse nonnegative matrix factorization

Abstract

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