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

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.