Duality between Feature Selection and Data Clustering
This work addresses the feature-selection problem for researchers in machine learning and data analysis, presenting a novel theoretical connection rather than an incremental improvement.
The paper tackles the feature-selection problem by formulating it from an information-theoretic perspective and shows it can be efficiently solved using an extension of the info-clustering paradigm, revealing a fundamental duality between feature selection and data clustering.
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.