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» Genetic Algorithms for Component Analysis
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NIPS
2004
15 years 1 months ago
Nonlinear Blind Source Separation by Integrating Independent Component Analysis and Slow Feature Analysis
In contrast to the equivalence of linear blind source separation and linear independent component analysis it is not possible to recover the original source signal from some unkno...
Tobias Blaschke, Laurenz Wiskott
CORR
2010
Springer
125views Education» more  CORR 2010»
14 years 12 months ago
Critical control of a genetic algorithm
Based on speculations coming from statistical mechanics and the conjectured existence of critical states, I propose a simple heuristic in order to control the mutation probability...
Raphaël Cerf
EACL
2006
ACL Anthology
15 years 1 months ago
Improving Probabilistic Latent Semantic Analysis with Principal Component Analysis
Probabilistic Latent Semantic Analysis (PLSA) models have been shown to provide a better model for capturing polysemy and synonymy than Latent Semantic Analysis (LSA). However, th...
Ayman Farahat, Francine Chen
ICIP
2005
IEEE
16 years 1 months ago
Largest-eigenvalue-theory for incremental principal component analysis
In this paper, we present a novel algorithm for incremental principal component analysis. Based on the LargestEigenvalue-Theory, i.e. the eigenvector associated with the largest ei...
Shuicheng Yan, Xiaoou Tang
GECCO
2007
Springer
124views Optimization» more  GECCO 2007»
15 years 3 months ago
Fitness-proportional negative slope coefficient as a hardness measure for genetic algorithms
The Negative Slope Coefficient (nsc) is an empirical measure of problem hardness based on the analysis of offspring-fitness vs. parent-fitness scatterplots. The nsc has been teste...
Riccardo Poli, Leonardo Vanneschi