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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/50913

Title: Active learning for semi-supervised clustering based on locally linear propagation reconstruction
Authors: Chin-Chun Chang
Po-Yi Lin
Contributors: 國立臺灣海洋大學:資訊工程學系
Keywords: Active learning
Semi-supervised clustering
Manifold learning
Locally linear embedding
Date: 2015-03
Issue Date: 2018-10-30T07:36:23Z
Publisher: Neural Networks
Abstract: Abstract: The success of semi-supervised clustering relies on the effectiveness of side information. To get effective side information, a new active learner learning pairwise constraints known as must-link and cannot-link constraints is proposed in this paper. Three novel techniques are developed for learning effective pairwise constraints. The first technique is used to identify samples less important to cluster structures. This technique makes use of a kernel version of locally linear embedding for manifold learning. Samples neither important to locally linear propagation reconstructions of other samples nor on flat patches in the learned manifold are regarded as unimportant samples. The second is a novel criterion for query selection. This criterion considers not only the importance of a sample to expanding the space coverage of the learned samples but also the expected number of queries needed to learn the sample. To facilitate semi-supervised clustering, the third technique yields inferred must-links for passing information about flat patches in the learned manifold to semi-supervised clustering algorithms. Experimental results have shown that the learned pairwise constraints can capture the underlying cluster structures and proven the feasibility of the proposed approach.
Relation: 63 pp.170-184
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/50913
Appears in Collections:[資訊工程學系] 期刊論文

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