Abstract
Feature extraction is an important preprocessing step which is encountered in many areas such as data mining, pattern recognition and scientific visualization. In this paper, a new method for sparse feature extraction using local manifold learning is proposed. Similarities in a neighborhood are first computed to explore local geometric structures, producing sparse feature representation. Based on the constructed similarity matrix, linear dimension reduction is applied to enhance similarities among the elements in the same class and extract optimal features for classification performances. Since it only computes similarities in a neighborhood, sparsity in the similarity matrix can give computational efficiency and memory savings. Experimental results demonstrate superior performances of the proposed method.
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© 2006 Springer-Verlag Berlin Heidelberg
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Park, C.H. (2006). Similarity-Based Sparse Feature Extraction Using Local Manifold Learning. In: Ng, WK., Kitsuregawa, M., Li, J., Chang, K. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2006. Lecture Notes in Computer Science(), vol 3918. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11731139_6
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DOI: https://doi.org/10.1007/11731139_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33206-0
Online ISBN: 978-3-540-33207-7
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