Semi-supervised clustering with metric learning: An adaptive kernel method

doi:10.1016/j.patcog.2009.11.005

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Semi-supervised clustering with metric learning: An adaptive kernel method

2010-04-01

103

PRE

Songcan Chen (Songcan Chen) *

Enliang Hu

张

张道强 (Daoqiang Zhang)

DOI：10.1016/j.patcog.2009.11.005

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Abstract

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Most existing representative works in semi-supervised clustering do not sufficiently solve the violation problem of pairwise constraints. On the other hand, traditional kernel methods for semi-supervised clustering not only face the problem of manually tuning the kernel parameters due to the fact that no sufficient supervision is provided, but also lack a measure that achieves better effectiveness of clustering. In this paper, we propose an adaptive Semi-supervised Clustering Kernel Method based on Metric learning (SCKMM) to mitigate the above problems. Specifically, we first construct an objective function from pairwise constraints to automatically estimate the parameter of the Gaussian kernel. Then, we use pairwise constraint-based K-means approach to solve the violation issue of constraints and to cluster the data. Furthermore, we introduce metric learning into nonlinear semi-supervised clustering to improve separability of the data for clustering. Finally, we perform clustering and metric learning simultaneously. Experimental results on a number of real-world data sets validate the effectiveness of the proposed method. (C) 2009 Elsevier Ltd. All rights reserved.

Keywords:

Metric learning

Pairwise constraint

Closure centroid

Semi-supervised clustering

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