The main idea is to measure the common information between the random variables by Watanabe's total correlation, and then find the hidden attributes of these random variables such that the common information is reduced the most given these attributes.
Oct 8, 2019
Feb 2, 2011 · Mutual information (MI) based upon information theory is one of the metrics used for measuring relevance of features. This paper analyses ...
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Feb 2, 2011 · Feature selection methods describe the set of methods that selects the features of data by keeping original features as such. The methods select ...
We develop an information theoretic framework for addressing feature selection in applications where the inference task is not specified in advance.
In this paper, we present a novel, information-theoretic algorithm for feature selection, which finds an optimal set of attributes by removing both irrelevant ...
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Abstract—In this paper, we propose an information-theoretic approach to design the functional representations to extract the hidden common structure shared ...
Feb 7, 2024 · We propose an information theoretic approach based on the Jensen Shannon divergence to quantify this robustness.
Dec 30, 2019 · In this paper, a graph-theoretic approach with step-by-step visualization is proposed in the context of supervised feature selection.
Abstract. Feature selection is a popular preprocessing step to reduce the dimensionality of the data while preserving the important information.
In this work we adopt this approach—instead of trying to define feature relevance indices, we derive them starting from a clearly specified objective function.