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A novel probabilistic label enhancement algorithm for multi-label distribution learning

A novel probabilistic label enhancement algorithm for multi-label distribution learning
A novel probabilistic label enhancement algorithm for multi-label distribution learning
We propose a novel probabilistic label enhancement algorithm, called PLEA, to solve challenging label distribution learning (LDL) for multi-label classification problems. We adopt the well-known maximum entropy model based label distribution learner. However, unlike the existing LDL algorithms based on the maximum entropy model, we propose to use manifold learning to enhance the label distribution learner. Specifically, the supervised information in the label manifold is utilized in the feature manifold space construction to improve the accuracy of feature extraction, while dramatically reducing the feature dimension. Then the robust linear regression is employed to estimate the label distributions associated with the extracted reduced-dimension features. Using the enhanced reduced-dimension features and their associated estimated label distributions in the maximum entropy model, the unknown true label distributions can be estimated more accurately, while imposing considerably lower computational complexity. We evaluate the proposed PLEA method on a wide-range artificial and high-dimensional real-world datasets. Experimental results obtained demonstrate that our proposed PLEA method has advantages in LDL accuracy and runtime performance, compared to the latest multi-label LDL approaches. The results also show that our PLEA compares favourably with the state-of-the-arts multi-label learning algorithms for classification tasks.
Entropy, Feature extraction, Manifold learning, Manifolds, Multi-label classification, Prediction algorithms, Training, Videos, label distribution learning, manifold learning, robust linear regression
1041-4347
Tan, Chao
c465770b-c23e-482d-97f9-9856b3f52c74
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Ji, Genlin
07169d5f-af46-4778-835d-d292432686a3
Geng, Xin
8285685e-9bd5-47a4-88f1-9ef09781e00f
Tan, Chao
c465770b-c23e-482d-97f9-9856b3f52c74
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Ji, Genlin
07169d5f-af46-4778-835d-d292432686a3
Geng, Xin
8285685e-9bd5-47a4-88f1-9ef09781e00f

Tan, Chao, Chen, Sheng, Ji, Genlin and Geng, Xin (2021) A novel probabilistic label enhancement algorithm for multi-label distribution learning. IEEE Transactions on Knowledge and Data Engineering. (doi:10.1109/TKDE.2021.3054465).

Record type: Article

Abstract

We propose a novel probabilistic label enhancement algorithm, called PLEA, to solve challenging label distribution learning (LDL) for multi-label classification problems. We adopt the well-known maximum entropy model based label distribution learner. However, unlike the existing LDL algorithms based on the maximum entropy model, we propose to use manifold learning to enhance the label distribution learner. Specifically, the supervised information in the label manifold is utilized in the feature manifold space construction to improve the accuracy of feature extraction, while dramatically reducing the feature dimension. Then the robust linear regression is employed to estimate the label distributions associated with the extracted reduced-dimension features. Using the enhanced reduced-dimension features and their associated estimated label distributions in the maximum entropy model, the unknown true label distributions can be estimated more accurately, while imposing considerably lower computational complexity. We evaluate the proposed PLEA method on a wide-range artificial and high-dimensional real-world datasets. Experimental results obtained demonstrate that our proposed PLEA method has advantages in LDL accuracy and runtime performance, compared to the latest multi-label LDL approaches. The results also show that our PLEA compares favourably with the state-of-the-arts multi-label learning algorithms for classification tasks.

Text
TKDE-2020-06-0580 - Accepted Manuscript
Restricted to Repository staff only until 28 January 2023.
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More information

Accepted/In Press date: 22 January 2021
e-pub ahead of print date: 28 January 2021
Keywords: Entropy, Feature extraction, Manifold learning, Manifolds, Multi-label classification, Prediction algorithms, Training, Videos, label distribution learning, manifold learning, robust linear regression

Identifiers

Local EPrints ID: 446513
URI: http://eprints.soton.ac.uk/id/eprint/446513
ISSN: 1041-4347
PURE UUID: 6f01b4cf-1c7d-490c-b378-bfce5f6d3db7

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Date deposited: 12 Feb 2021 17:30
Last modified: 19 Feb 2021 17:33

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