2008 LearningAMahalanobisDistMetric

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Subject Headings: Distance Function, Distance Function Learning, Mahalanobis Distance, Global Optimization Algorithm, Clustering Algorithm, Interactive Image Segmentation Task, Face Pose Estimation Task.

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Abstract

Distance metric is a key issue in many machine learning algorithms. This paper considers a general problem of learning from pairwise constraints in the form of must-links and cannot-links. As one kind of side information, a must-link indicates the pair of the two data points must be in a same class, while a cannot-link indicates that the two data points must be in two different classes. Given must-link and cannot-link information, our goal is to learn a Mahalanobis distance metric. Under this metric, we hope the distances of point pairs in must-links are as small as possible and those of point pairs in cannot-links are as large as possible. This task is formulated as a constrained optimization problem, in which the global optimum can be obtained effectively and efficiently. Finally, some applications in data clustering, interactive natural image segmentation and face pose estimation are given in this paper. Experimental results illustrate the effectiveness of our algorithm.,


 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2008 LearningAMahalanobisDistMetricShiming Xiang
Feiping Nie
Changshui Zhang
Learning a Mahalanobis Distance Metric for Data Clustering and ClassificationPattern Recognition Taskhttp://feipingnie.googlepages.com/pr dml.pdf10.1016/j.patcog.2008.05.0182008
AuthorShiming Xiang +, Feiping Nie + and Changshui Zhang +
doi10.1016/j.patcog.2008.05.018 +
journalPattern Recognition +
titleLearning a Mahalanobis Distance Metric for Data Clustering and Classification +
titleUrlhttp://feipingnie.googlepages.com/pr dml.pdf +
year2008 +