关键词: 特征选择, 共享特征子空间, 多源适应学习, 多标签学习

Abstract: Multi-source adaptation learning is an effective machine learning method to boost the target learning performance of interest. For multi-label visual classification applications, motivated by the advances of existing arts such as feature selection and subspace learning, this paper proposes a novel Multi-source Adaptation Multi-Label classification framework via Joint Feature Selection and shared feature Subspace learning（MAML-JFSS）. Specifically, the proposed framework simultaneously considers in the classical graph Laplacian regularization semi-supervised learning scheme such important issues as the target visual feature preprocessing, the correlation among different labels encoded in some optimal shared feature subspace, and leveraging the discriminant information from multiple related source domains, and then integrates them into unified learning model. It shows by theory that the optimal solution of the proposed framework can be obtained by performing a generalized eigen-decomposition task. And then the implementing algorithm and the convergence analysis for MAML-JFSS are given. Extensive experiments on two real-world multi-label visual classification tasks are conducted. The experimental results show that the proposed algorithm is effective and can obtain comparable or even superior to related works.

Key words: feature selection, shared feature subspace, multi-source adaptation learning, multi-label learning