Local margin based semi-supervised discriminant embedding for visual recognition

Authors:
Feng Pan;Jiandong Wang;Xiaohui Lin
Affiliations:
College of Information Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016 Jiangsu, China and College of Management, Shenzhen University, Shenzhen, 518060 G ...;College of Information Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016 Jiangsu, China;State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China and College of Information Engineer, Shenzhen University, Guangdong, ...
Venue:
Neurocomputing
Year:
2011

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Abstract

Most manifold learning algorithms adopt the k nearest neighbors function to construct the adjacency graph. However, severe bias may be introduced in this case if the samples are not uniformly distributed in the ambient space. In this paper a semi-supervised dimensionality reduction method is proposed to alleviate this problem. Based on the notion of local margin, we simultaneously maximize the separability between different classes and estimate the intrinsic geometric structure of the data by both the labeled and unlabeled samples. For high-dimensional data, a discriminant subspace is derived via maximizing the cumulative local margins. Experimental results on high-dimensional classification tasks demonstrate the efficacy of our algorithm.