Abstract
In this paper, a semi-supervised dimensionality reduction algorithm for feature extraction, named LRDPSSDR, is proposed by combining local reconstruction with dissimilarity preserving. It focuses on local and global structure based on labeled and unlabeled samples in learning process. It sets the edge weights of adjacency graph by minimizing the local reconstruction error and preserves local geometric structure of samples. Besides, the dissimilarity between samples is represented by maximizing global scatter matrix so that the global manifold structure can be preserved well. Comprehensive comparison and extensive experiments demonstrate the effectiveness of LRDPSSDR.
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Acknowledgments
We wish to thank the National Science Foundation of China under Grant No. 61175111, and the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant No. 10KJB510027 for supporting this work.
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Li, F., Wang, Z., Zhou, Z., Xue, W. (2013). Local Reconstruction and Dissimilarity Preserving Semi-supervised Dimensionality Reduction. In: Sun, Z., Deng, Z. (eds) Proceedings of 2013 Chinese Intelligent Automation Conference. Lecture Notes in Electrical Engineering, vol 256. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38466-0_13
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DOI: https://doi.org/10.1007/978-3-642-38466-0_13
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