Modified orthogonal discriminant projection for classification

Authors:
Shanwen Zhang;Ying-Ke Lei;Yan-Hua Wu;Jun-An Yang
Affiliations:
Institute of Intelligent Machines, Chinese Academy of Sciences, P.O. Box 1130, Hefei, Anhui 230031, China;Institute of Intelligent Machines, Chinese Academy of Sciences, P.O. Box 1130, Hefei, Anhui 230031, China and Department of Automation, University of Science and Technology of China, Hefei, Anhui ...;Electronic Engineering Institute, Hefei, Anhui 230027, China;Electronic Engineering Institute, Hefei, Anhui 230027, China
Venue:
Neurocomputing
Year:
2011

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Abstract

From the perspective of manifold learning, the weight between two nodes of graph plays an indispensable role, which provides the similarity between pairwise nodes, and can effectively reveal the intrinsic relationship between data classes. In the original Locality Preserving Projections (LPP), Unsupervised Discriminant Projection (UDP), Orthogonal LPP (OLPP), and other spectral mapping methods, the weight between two points is usually defined as a heat kernel or simply 0-1 weight, which cannot effectively reflect the sample class information. In Orthogonal Discriminant Projection (ODP), the weight between two points was defined based on their local information and class information, but it is not a monotonically decreasing with the increase of the distance between two nodes, so it is not very sound. In this paper, we first analyze the defect of the weight in ODP, then propose a novel weight measure between two nodes of a graph by combining their label information and local information, finally present a modified ODP algorithm following the ODP technique. The modified ODP algorithm can explore the intrinsic structure of original data and enhance the classification ability. The experimental results show that the modified ODP algorithm is effective and feasible.