Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Kernel independent component analysis
The Journal of Machine Learning Research
Convex Optimization
Sequential fixed-point ica based on mutual information minimization
Neural Computation
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Density Ratio Estimation: A New Versatile Tool for Machine Learning
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
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Accurately evaluating statistical independence among random variables is a key component of Independent Component Analysis (ICA). In this paper, we employ a squared-loss variant of mutual information as an independence measure and give its estimation method. Our basic idea is to estimate the ratio of probability densities directly without going through density estimation, by which a hard task of density estimation can be avoided. In this density-ratio approach, a natural cross-validation procedure is available for model selection. Thanks to this, all tuning parameters such as the kernel width or the regularization parameter can be objectively optimized. This is a highly useful property in unsupervised learning problems such as ICA. Based on this novel independence measure, we develop a new ICA algorithm named Least-squares Independent Component Analysis (LICA).