Group preserving label embedding for multi-label classification

Highlights：

• In this paper, we study the embedding of labels together with the group information with an objective to build an efficient multi-label classification.

• We assume the existence of a low-dimensional space onto which the feature vectors and label vectors can be embedded.

• We ensure that labels belonging to the same group share the same sparsity pattern in their low-rank representations.

• The proposed method has three major stages namely (1) Identification of groups of labels; (2) Sparsity-invariant embedding of label groups; and (3) Embedding of feature matrix to the same low-rank space.

• Extensive comparative studies validate the effectiveness of the proposed method against the state-of-the-art multi-label learning approaches.

摘要

•In this paper, we study the embedding of labels together with the group information with an objective to build an efficient multi-label classification.•We assume the existence of a low-dimensional space onto which the feature vectors and label vectors can be embedded.•We ensure that labels belonging to the same group share the same sparsity pattern in their low-rank representations.•The proposed method has three major stages namely (1) Identification of groups of labels; (2) Sparsity-invariant embedding of label groups; and (3) Embedding of feature matrix to the same low-rank space.•Extensive comparative studies validate the effectiveness of the proposed method against the state-of-the-art multi-label learning approaches.

论文评审过程：Received 17 August 2017, Revised 28 October 2018, Accepted 7 January 2019, Available online 15 January 2019, Version of Record 22 January 2019.