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Multi-view multi-label learning with double orders manifold preserving

Multi-view multi-label learning with double orders manifold preserving In multi-view multi-label learning, each instance has multiple heterogeneous views and is marked with a collection of non-exclusive discrete labels. This type of data is usually subject to dimensional catastrophe. Previous multi-view multi-label works look for a low-dimensional shared subspace to tackle this problem. However, these methods ignore the global structural information of the original feature space during dimension reduction. In this paper, we propose Multi-view Multi-label learning with Double Orders Manifold Preserving (MMDOM). MMDOM utilizes manifold preserving constraint to guide the formation of low-dimensional shared subspace. To obtain exact manifold preserving, the first-order and the second-order similarity matrices are both introduced to explore the local and global structural information of the original feature space. Experiments on various benchmark datasets demonstrate the superior effectiveness of MMDOM against state-of-the-art methods. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Intelligence Springer Journals

Multi-view multi-label learning with double orders manifold preserving

Yin, Jun; Zhang, Wentao
Applied Intelligence , Volume 53 (12) – Jun 1, 2023
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References (47)

Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
ISSN
0924-669X
eISSN
1573-7497
DOI
10.1007/s10489-022-04242-4
Publisher site
See Article on Publisher Site

Abstract

In multi-view multi-label learning, each instance has multiple heterogeneous views and is marked with a collection of non-exclusive discrete labels. This type of data is usually subject to dimensional catastrophe. Previous multi-view multi-label works look for a low-dimensional shared subspace to tackle this problem. However, these methods ignore the global structural information of the original feature space during dimension reduction. In this paper, we propose Multi-view Multi-label learning with Double Orders Manifold Preserving (MMDOM). MMDOM utilizes manifold preserving constraint to guide the formation of low-dimensional shared subspace. To obtain exact manifold preserving, the first-order and the second-order similarity matrices are both introduced to explore the local and global structural information of the original feature space. Experiments on various benchmark datasets demonstrate the superior effectiveness of MMDOM against state-of-the-art methods.

Journal

Applied Intelligence – Springer Journals

Published: Jun 1, 2023

Keywords: Multi-view; Multi-label; Subspace learning; Manifold learning

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