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Published in: International Journal of Machine Learning and Cybernetics 6/2019

27-03-2018 | Original Article

Semi-supervised discriminant Isomap with application to visualization, image retrieval and classification

Authors: Rui Huang, Guopeng Zhang, Junli Chen

Published in: International Journal of Machine Learning and Cybernetics | Issue 6/2019

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Abstract

As one of the most promising nonlinear unsupervised dimensionality reduction (DR) technique, the Isomap reveals the intrinsic geometric structure of manifold by preserving geodesic distance of all data pairs. Recently, some supervised versions of Isomap have been presented to guide the manifold learning and increase the discriminating capability. However, the performance may deteriorate when there is no sufficient prior information available. Hence, a novel semi-supervised discriminant Isomap (SSD-Isomap) is proposed in the paper. First, two pairwise constraints including must-link and likely-link (LL) are used to depict the neighborhoods of data points. Then, two graphs are constructed based on the two constraints, and distances between points belonging to the LL constraint are modified by a scale parameter. Finally, the geodesic distance metric is obtained based on the graphs, and the corresponding optimal nonlinear subspace is sought. The performance of SSD-Isomap is evaluated by extensive experiments of data visualization, image retrieval and classification. Compared with other state-of-the-art DR methods, SSD-Isomap presents more accurate and robust results.

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Metadata
Title
Semi-supervised discriminant Isomap with application to visualization, image retrieval and classification
Authors
Rui Huang
Guopeng Zhang
Junli Chen
Publication date
27-03-2018
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 6/2019
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-018-0809-6

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