关键词: 多视图学习, 线性鉴别分析, 人脸识别, 子空间学习

Abstract: In order to efficiently integrate multi-view information and make optimal view is dominant in multi-view intact subspace learning, a multi-view collaboration intact subspace learning scheme is proposed. Furthermore, aiming to enable the intact feature representations of objects having more powerful discriminability in latent intact subspace, Fisher discriminant analysis is introduced into intact subspace learning. By employing Fisher discriminant analysis, the within-class scatter of intact feature representations is minimized and the between-class scatter of intact feature representations is maximized, simultaneously. By combining multi-view collaboration intact subspace learning scheme and Fisher discriminant analysis together, an algorithm named robust multi-view collaboration intact discriminant subspace learning is proposed. Experiment results demonstrate that the proposed algorithm can efficiently integrate multi-view information and mine discriminant information. And the proposed algorithm is an effective multi-view intact discriminant subspace learning algorithm.

Key words: multi-view learning, linear discriminant analysis, face recognition, subspace learning