Abstract
AbstractFeature selection and dimensionality reduction is important for high dimensional signal processing and pattern recognition problems. Feature selection can be achieved by filter approach, in which certain criteria must be optimized. By using mutual information (MI) between feature vectors and class labels as the criterion, we proposed an ICA-MI framework for feature selection. In this paper, we will compare the linear ICA and local linear ICA for the accuracy of MI estimation, and study the bias-variance trade-off on feature projections and ranking.KeywordsFeature VectorFeature SelectionMutual InformationClass LabelGaussian Mixture ModelThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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