Duality between Feature Selection and Data Clustering

TL;DR

This paper reveals a fundamental duality between feature selection and data clustering by formulating feature selection through an information-theoretic lens and extending info-clustering methods.

Contribution

It introduces a novel information-theoretic framework that connects feature selection with data clustering via an extension of info-clustering.

Findings

Feature selection can be efficiently solved using info-clustering techniques.

A fundamental duality exists between feature selection and data clustering.

The duality stems from the principal partition and lattice of partitions in combinatorial optimization.

Abstract

The feature-selection problem is formulated from an information-theoretic perspective. We show that the problem can be efficiently solved by an extension of the recently proposed info-clustering paradigm. This reveals the fundamental duality between feature selection and data clustering,which is a consequence of the more general duality between the principal partition and the principal lattice of partitions in combinatorial optimization.