Semi-Supervised Information-Maximization Clustering

TL;DR

This paper introduces a semi-supervised clustering algorithm based on information maximization that effectively incorporates prior must-link and cannot-link constraints, offering analytical solutions and parameter tuning.

Contribution

It extends an existing unsupervised information-maximization clustering method to semi-supervised settings with efficient eigendecomposition and systematic parameter optimization.

Findings

The method is computationally efficient with analytical eigendecomposition.

It effectively incorporates must-link and cannot-link constraints.

Experiments demonstrate its practical usefulness.

Abstract

Semi-supervised clustering aims to introduce prior knowledge in the decision process of a clustering algorithm. In this paper, we propose a novel semi-supervised clustering algorithm based on the information-maximization principle. The proposed method is an extension of a previous unsupervised information-maximization clustering algorithm based on squared-loss mutual information to effectively incorporate must-links and cannot-links. The proposed method is computationally efficient because the clustering solution can be obtained analytically via eigendecomposition. Furthermore, the proposed method allows systematic optimization of tuning parameters such as the kernel width, given the degree of belief in the must-links and cannot-links. The usefulness of the proposed method is demonstrated through experiments.