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Multi-label space reshape for semantic-rich label-specific features learning

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Abstract

Existing label-specific features learning techniques mainly use embedding-based researching methods. However, there exist many problems such as inadequate consideration of label semantics, the sparseness of selected features and so on. Herein, the LSR-LSF (multi-label space reshape for semantic-rich label-specific features learning) algorithm is proposed in this paper to solve these problems. Firstly, the sparse logical matrix is constructed into a numerical label matrix through the label propagation dependency matrix. Secondly, constraint propagation is added to avoid the differences that may exist in the label matrix before or after the reshaping process. The alternate iteration method is used to obtain the numerical label vector. At the same time, the reshaped label correlation matrix is constructed by the cosine similarity to constrain the solution space. Then, measuring whether the learning ability of label-specific features has been improved. Finally, extensive experiments on benchmark datasets show the superiority of LSR-LSF over other state-of-the-art label-specific features learning methods.

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Acknowledgments

This work was supported by the National Natural Science Foundation of Anhui under Grant 2108085MF216 and Key Laboratory of Data Science and Intelligence Application, Fujian Province University (NO. D202005), and Key Laboratory of Intelligent Computing & Signal Processing, Ministry of Education (Anhui University) (No.2020A003).

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Authors and Affiliations

  1. School of Computer and Information, Anqing Normal University, Anqing, 246011, Anhui, China

    Yusheng Cheng, Chao Zhang & Shufang Pang

  2. The University Key Laboratory of Intelligent Perception and Computing of Anhui Province, Anqing, 246133, China

    Yusheng Cheng & Shufang Pang

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  1. Yusheng Cheng
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  2. Chao Zhang
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  3. Shufang Pang
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Correspondence to Yusheng Cheng.

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Cheng, Y., Zhang, C. & Pang, S. Multi-label space reshape for semantic-rich label-specific features learning. Int. J. Mach. Learn. & Cyber. 13, 1005–1019 (2022). https://doi.org/10.1007/s13042-021-01432-3

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  • DOI: https://doi.org/10.1007/s13042-021-01432-3

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