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Manifold contraction for semi-supervised classification

science china(Information sciences) 2010年第6期53卷 1170-1187页

作者： hu enliang 1,2 , chen songcan 1 & yin xuesong 1 1 school of computer science & engineering, nanjing university of aeronautics & astronautics, nanjing 210016, china 2 school of Mathematics, Yunnan Normal university, Kunming 650092, china 1. school of computer science & engineering Nanjing University of Aeronautics & Astronautics Nanjing 210016 China2. School of Mathematics Yunnan Normal University Kunming 650092 China

The generalization ability of classification is often closely related to both the intra-class compactness and the inter-class *** to the fact that many current dimensionality reduction methods, regarded as a pre-processor, often lead to the poor classification performance on real-life data, in this paper, a new data preprocessing technique called manifold contraction(MC) is proposed for the classification-oriented learning *** main motivation behind MC lies in seeking a proper mapping of contracting the given multiple-manifold data such that the ratio of the intra-class to the inter-class scatters is ***, in order to properly control the contraction level in MC, an adaptive MC(AMC) criterion is developed in the semi-supervised *** to its generality, MC can be not only applied in original space and reproducing kernel Hilbert space(RKHS), but also easily incorporated with dimensionality reduction method for further improvement of classification *** final experimental results show that MC, as a data preprocessor, is effective and promising in the subsequent classification learning, especially in small-size labeled sample case.

关键词： manifold learning dimensionality reduction lower-dimensional embedding semi-supervised learning classification manifold contraction adaptive manifold contraction

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