Linear Discriminant Analysis (LDA) is a popular data-analytic tool for studying the class relationship between data points. A major disadvantage of LDA is that it fails to discover the local geometrical structure of the data manifold. In this paper, we introduce a novel linear algorithm for discriminant analysis, called {\bf Locality Sensitive Discriminant Analysis} (LSDA). When there is no sufficient training samples, local structure is generally more important than global structure for discriminant analysis. By discovering the local manifold structure, LSDA finds a projection which maximizes the margin between data points from different classes at each local area. Specifically, the data points are mapped into a subspace in which the nearby points with the same label are close to each other while the nearby points with different labels are far apart. Experiments carried out on several standard face databases show a clear improvement over the results of LDA-based recognition.

Authors

• Deng Cai Bytedance Seed, China
• Xiaofei He State Key Lab of CAD&CG, Zhejiang University Fabu Inc.
• Kun Zhou State Key Lab of CAD&CG, Zhejiang University, Hangzhou, China
• Jiawei Han University of Illinois Urbana-Champaign
• Hujun Bao Zhejiang University

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