Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Practical Genetic Algorithms with CD-ROM
Practical Genetic Algorithms with CD-ROM
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Projected Gradient Methods for Nonnegative Matrix Factorization
Neural Computation
SVD based initialization: A head start for nonnegative matrix factorization
Pattern Recognition
Nonnegative matrix factorization with Gaussian process priors
Computational Intelligence and Neuroscience - Advances in Nonnegative Matrix and Tensor Factorization
SIAM Journal on Matrix Analysis and Applications
Feeding the fish - weight update strategies for the fish school search algorithm
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part II
Fireworks algorithm for optimization
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I
Feeding the fish - weight update strategies for the fish school search algorithm
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part II
Subtractive clustering for seeding non-negative matrix factorizations
Information Sciences: an International Journal
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The nonnegative matrix factorization (NMF) is a boundconstrained low-rank approximation technique for nonnegative multivariate data. NMF has been studied extensively over the last years, but an important aspect which only has received little attention so far is a proper initialization of the NMF factors in order to achieve a faster error reduction. Since the NMF objective function is usually non-differentiable, discontinuous, and may possess many local minima, heuristic search algorithms are a promising choice as initialization enhancers for NMF. In this paper we investigate the application of five population based algorithms (genetic algorithms, particle swarm optimization, fish school search, differential evolution, and fireworks algorithm) as new initialization variants for NMF. Experimental evaluation shows that some of them are well suited as initialization enhancers and can reduce the number of NMF iterations needed to achieve a given accuracy. Moreover, we compare the general applicability of these five optimization algorithms for continuous optimization problems, such as the NMF objective function.