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论文类型：期刊论文

发表时间：2013-01-01

发表刊物：NEUROCOMPUTING

收录刊物：SCIE、EI、Scopus

卷号：99

页面范围：423-438

ISSN号：0925-2312

关键字：Principal Component Analysis; Local tangent space; Similarity embedding; Cosine metric

摘要：In recent times the dimensionality reduction technique has been widely exploited in pattern recognition and data mining. The global linear algorithms characterize the local sampling information, thereby making it superior to Principal Component Analysis (PCA). However, these algorithms are all inefficient for extracting the local data feature, which leads to incomplete learning. A new global linear algorithm is proposed in this paper, which is named Maximal Similarity Embedding (MSE). The preserving local feature of this new algorithm makes it distinct from most other methods. The MSE algorithm utilizes the Cosine Metric to describe the geometric characteristics of neighborhood and thus seeks to maximize the global similarity for dimensionality reduction. This new proposal method is robust for sparse dataset and naturally helps in avoiding the problem of small sample size cases. Extensive experiments have been performed on both synthetic and real-world images to prove the efficiency of the MSE algorithm. (C) 2012 Elsevier B.V. All rights reserved.