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Study Features via Exploring Distribution Structure
In this paper, we present a novel framework for data redundancy measurement based on probabilistic modeling of datasets, and a new criterion for redundancy detection that is resilient to noise.
Feature Selection via Maximizing Distances between Class Conditional Distributions
Our framework directly explores the discriminative information of features in the sense of distributions for supervised classification.
A Contrast Based Feature Selection Algorithm for High-dimensional Data set in Machine Learning
Feature selection is an important process in machine learning and knowledge discovery.
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