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A maximum entropy and maximum likelihood criteria for feature selection from multivariate data
A maximum entropy and maximum likelihood criteria for feature selection from multivariate data
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机译:从多元数据中选择特征的最大熵和最大似然准则
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摘要
Improvements in speech recognition systems are achieved by considering projections of the high dimensional data on lower dimensional subspaces, subsequently by estimating the univariate probability densities via known univariate techniques, and then by reconstructing the density in the original higher dimensional space from the collection of univariate densities so obtained. The reconstructed density is by no means unique unless further restrictions on the estimated density are imposed. The variety of choices of candidate univariate densities as well as the choices of subspaces on which to project the data including their number further add to this non-uniqueness. Probability density functions are then considered that maximize certain optimality criterion as a solution to this problem. Specifically, those probability density functions that either maximize the entropy functional, or alternatively, the likelihood associated with the data are considered.
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