title: Semi-supervised information-maximization clustering.
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.
authors: Politecnico di Milano, Milano, Italy. Electronic address: daniele.calandriello@mail.polimi.it.
authors: Daniele
authors: D
authors: Calandriello
authors: Tokyo Institute of Technology, Tokyo, Japan. Electronic address: gang@sg.cs.titech.ac.jp.
authors: Gang
authors: G
authors: Niu
authors: Tokyo Institute of Technology, Tokyo, Japan. Electronic address: sugi@cs.titech.ac.jp.
authors: Masashi
authors: M
authors: Sugiyama
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis
keywords: Clustering; Information maximization; Semi-supervised; Squared-loss mutual information