An Information-Theoretic Approach to Universal Feature Selection in High-Dimensional Inference

2017 IEEE INTERNATIONAL SYMPOSIUM ON INFORMATION THEORY (ISIT), pp. 1336-1340, 2017.

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Abstract:

We develop an information theoretic framework for addressing feature selection in applications where the inference task is not specified in advance and the data is from a large alphabet. We introduce a natural notion of universality for such problems, and show that locally optimal solutions are straightforward to obtain, admit natural int...More

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