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Multilabel distribution learning based on multi-output regression and manifold learning

Multilabel distribution learning based on multi-output regression and manifold learning
Multilabel distribution learning based on multi-output regression and manifold learning
Real-world multilabel data are high dimensional, and directly using them for label distribution learning will incur extensive computational costs. We propose a multilabel distribution learning algorithm based on multi-output regression through manifold learning, referred to as MDLRML. By exploiting smooth, similar spaces' information provided by the samples' manifold learning and the label distribution learning, we link the two spaces' manifolds. This facilitates using the topological relationship of the manifolds in the feature space to guide the manifold construction of the label space. The smoothest regression function is used to fit the manifold data, and a locally constrained multi-output regression is designed to improve the data's local fitting. Based on the regression results, we enhance the logical labels into the label distributions, thereby mining and revealing the label's hidden information regarding importance or significance. Extensive experimental results using real-world multilabel data sets show that the proposed MDLRML algorithm significantly improves the multilabel distribution learning accuracy and efficiency over several existing state-of-the-art schemes.
2168-2267
1-14
Tan, Chao
702e3af2-6bbb-49f1-af99-8ec8f26d588b
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Ji, Genlin
cf3bf9dd-8e1b-484e-9ccb-57f85b8b5b8e
Geng, Xin
e8618d01-3413-4e73-9571-5c7f12ae1eed
Tan, Chao
702e3af2-6bbb-49f1-af99-8ec8f26d588b
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Ji, Genlin
cf3bf9dd-8e1b-484e-9ccb-57f85b8b5b8e
Geng, Xin
e8618d01-3413-4e73-9571-5c7f12ae1eed

Tan, Chao, Chen, Sheng, Ji, Genlin and Geng, Xin (2020) Multilabel distribution learning based on multi-output regression and manifold learning. IEEE Transactions on Cybernetics, 1-14. (In Press)

Record type: Article

Abstract

Real-world multilabel data are high dimensional, and directly using them for label distribution learning will incur extensive computational costs. We propose a multilabel distribution learning algorithm based on multi-output regression through manifold learning, referred to as MDLRML. By exploiting smooth, similar spaces' information provided by the samples' manifold learning and the label distribution learning, we link the two spaces' manifolds. This facilitates using the topological relationship of the manifolds in the feature space to guide the manifold construction of the label space. The smoothest regression function is used to fit the manifold data, and a locally constrained multi-output regression is designed to improve the data's local fitting. Based on the regression results, we enhance the logical labels into the label distributions, thereby mining and revealing the label's hidden information regarding importance or significance. Extensive experimental results using real-world multilabel data sets show that the proposed MDLRML algorithm significantly improves the multilabel distribution learning accuracy and efficiency over several existing state-of-the-art schemes.

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CYB-E-2019-06-1270 - Accepted Manuscript
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Accepted/In Press date: 21 September 2020

Identifiers

Local EPrints ID: 444107
URI: http://eprints.soton.ac.uk/id/eprint/444107
ISSN: 2168-2267
PURE UUID: 6c5b7010-31d5-4381-aa83-a70eb7a372c4

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Date deposited: 25 Sep 2020 16:33
Last modified: 21 Nov 2020 05:01

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Contributors

Author: Chao Tan
Author: Sheng Chen
Author: Genlin Ji
Author: Xin Geng

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