Abstract High dimensional data is sensitive to noise and the curse of dimensionality problem appears easily. A local discriminant projection algorithm based on random subspace(RSLDP) is proposed. The attributes of original high dimensional data are selected by random subspace method to generate a low dimensional subspace, and the nearest neighbor graphs are constructed in the low dimensional subspace. Thus, the influence of noise is reduced effectively. By RSLDP, the local inter-class weighted scatter is maximized, the local intra-class weighted scatter is minimized, and simultaneously the local scatter on data is minimized as well. Consequently, the performance of local maximal margin discriminant embedding (LMMDE) algorithm is improved.The relationship between the focusing point and its inter-class/intra-class nearest neighbor center point is well characterized by RSLDP. The effectiveness of the proposed algorithm is verified by the experiments on CMU PIE and AR face datasets.
Fund:Supported by National Natural Science Foundation of China(No.61272273), Graduate Student Innovation Project of Jiangsu Province(No.CXLX11_0418)
About author:: (HAN Lu, born in 1982, Ph.D. candidate, lecturer. Her research interests include pa-ttern recognition and machine learning.) (WU Fei, born in 1989, Ph.D., lecturer. His research interests include pattern recognition, machine learning and software enginee-ring.) (JING Xiaoyuan(Corresponding author), born in 1971, Ph.D., professor. His research interests include pattern recognition, image processing and machine learning.)
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