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

• Published in: International journal of machine learning and cybernetics Vol. 13; no. 4; pp. 1005 - 1019
• Main Authors: Cheng, Yusheng, Zhang, Chao, Pang, Shufang
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2022; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Classification; Complex Systems; Computational Intelligence; Control; Correlation analysis; Data compression; Datasets; Engineering; Hypotheses; Iterative methods; Labels; Machine learning; Mathematical analysis; Mechatronics; Original Article; Pattern Recognition; Propagation; Research methodology; Robotics; Semantics; Solution space; Systems Biology; Multi-label learning; Numerical labels; Label propagation; Label space reshape; Label-specific features
• ISSN: 1868-8071; 1868-808X
• DOI: 10.1007/s13042-021-01432-3

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.