Please use this identifier to cite or link to this item: https://doi.org/10.1109/TNN.2011.2107919
Title: Toward the optimization of normalized graph Laplacian
Authors: Xie, B.
Wang, M. 
Tao, D.
Keywords: Graph
Laplacian
metric learning
semisupervised learning
Issue Date: 2011
Source: Xie, B.,Wang, M.,Tao, D. (2011). Toward the optimization of normalized graph Laplacian. IEEE Transactions on Neural Networks 22 (4) : 660-666. ScholarBank@NUS Repository. https://doi.org/10.1109/TNN.2011.2107919
Abstract: Normalized graph Laplacian has been widely used in many practical machine learning algorithms, e.g., spectral clustering and semisupervised learning. However, all of them use the Euclidean distance to construct the graph Laplacian, which does not necessarily reflect the inherent distribution of the data. In this brief, we propose a method to directly optimize the normalized graph Laplacian by using pairwise constraints. The learned graph is consistent with equivalence and nonequivalence pairwise relationships, and thus it can better represent similarity between samples. Meanwhile, our approach, unlike metric learning, automatically determines the scale factor during the optimization. The learned normalized Laplacian matrix can be directly applied in spectral clustering and semisupervised learning algorithms. Comprehensive experiments demonstrate the effectiveness of the proposed approach. © 2011 IEEE.
Source Title: IEEE Transactions on Neural Networks
URI: http://scholarbank.nus.edu.sg/handle/10635/39474
ISSN: 10459227
DOI: 10.1109/TNN.2011.2107919
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