Three criteria are analyzed: 1 maximization of the geometric mean of the KL divergences, 2 maximization of the geometric mean of the normalized KL divergences, and 3 the combination of 1 and 2. Preliminary experimental results based on synthetic data, UCI Machine Learning Repository, and handwriting digits show that the third criterion is a potential discriminative subspace selection method, which significantly reduces the class separation problem in comparing with the linear dimensionality reduction step in FLDA and its several representative extensions.
Article :. Date of Publication: 31 March PubMed ID: DOI: Need Help? If separate classes are sampled from Gaussian distributions, all with identical covariance matrices, then the linear dimensionality reduction step in FLDA maximizes the mean value of the Kullback-Leibler KL divergences between different classes. Based on this viewpoint, the geometric mean for subspace selection is studied in this paper.
Three criteria are analyzed: 1 maximization of the geometric mean of the KL divergences, 2 maximization of the geometric mean of the normalized KL divergences, and 3 the combination of 1 and 2. Preliminary experimental results based on synthetic data, UCI Machine Learning Repository, and handwriting digits show that the third criterion is a potential discriminative subspace selection method, which significantly reduces the class separation problem in comparing with the linear dimensionality reduction step in FLDA and its several representative extensions.
Abstract: Subspace selection approaches are powerful tools in pattern classification and data visualization. Please use this identifier to cite or link to this item:. Faculty of Engineering and Information Technology. General Collection. Not enough data to produce graph. Adobe PDF. Li, X. Wu, X. Maybank, SJ. Subspace selection approaches are powerful tools in pattern classification and data visualization. Data Interpretation, Statistical.
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