Abstract In most of the existing manifold learning algorithms, the geometry structure of the data instances is preserved, but the label information is ignored. Therefore, the application of manifold learning algorithms in data classification is limited. In this paper, a semi-supervised manifold learning algorithm based on neighborhood components analysis is proposed. A distance metric matrix is learned by using neighbor components analysis and local neighbors of the sample points are selected by using the new distance metric. The local geometric structures of the sample points and their neighbors are constructed under the new distance metric, and the local geometric structures are preserved in the low-dimensional embedding coordinates of the sample points. The classification experiments conducted on three different datasets demonstrate the efficiency of the proposed algorithm.
About author:: (LI Xueqing, born in 1989, master student. Her research interests include machine learning, manifold learning and manifold alignment.) (WANG Jing(Corresponding author), born in 1981, Ph.D., professor. His research interests include manifold learning, recommendation systems and matrix computation.) (DU Jixiang, born in 1977, Ph.D., profe-ssor. His research interests include pattern recognition and machine learning.)
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2017, Vol. 30 
