Sparse nonnegative matrix factorization applied to microarray data sets
2006
Nonnegative Matrix Factorization (NMF) has proven to be a useful tool for the analysis of nonnegative multivariate data. However, it is known not to lead to unique results when applied to nonnegative Blind Source Separation (BSS) problems. In this paper we present first results of an extension to the NMF algorithm which solves the BSS problem when the underlying sources are sufficiently sparse. As the proposed target function has many local minima, we use a genetic algorithm for its minimization.
Keywords:
- Discrete mathematics
- Blind signal separation
- Principal component analysis
- Mathematical optimization
- Matrix decomposition
- Sparse matrix
- Independent component analysis
- Maxima and minima
- Machine learning
- Artificial intelligence
- Non-negative matrix factorization
- Source separation
- Mathematics
- Algorithm
- Genetic algorithm
- Computer science
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