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Document Type

Journal Article

Department/Unit

Department of Computer Science

Title

Semi-supervised maximum margin clustering with pairwise constraints

Language

English

Abstract

The pairwise constraints specifying whether a pair of samples should be grouped together or not have been successfully incorporated into the conventional clustering methods such as k-means and spectral clustering for the performance enhancement. Nevertheless, the issue of pairwise constraints has not been well studied in the recently proposed maximum margin clustering (MMC), which extends the maximum margin framework in supervised learning for clustering and often shows a promising performance. This paper therefore proposes a pairwise constrained MMC algorithm. Based on the maximum margin idea in MMC, we propose a set of effective loss functions for discouraging the violation of given pairwise constraints. For the resulting optimization problem, we show that the original nonconvex problem in our approach can be decomposed into a sequence of convex quadratic program problems via constrained concave-convex procedure (CCCP). Subsequently, we present an efficient subgradient projection optimization method to solve each convex problem in the CCCP sequence. Experiments on a number of real-world data sets show that the proposed constrained MMC algorithm is scalable and outperforms the existing constrained MMC approach as well as the typical semi-supervised clustering counterparts. © 1989-2012 IEEE.

Keywords

constrained concave-convex procedure., maximum margin clustering, pairwise constraints, Semi-supervised clustering

Publication Date

2012

Source Publication Title

IEEE Transactions on Knowledge and Data Engineering

Volume

24

Issue

5

Start Page

926

End Page

939

Publisher

Institute of Electrical and Electronics Engineers

ISSN (print)

10414347

ISSN (electronic)

15582191

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