Abstract
As a widely used linear dimensionality reduction technique, Locality Preserving Projections (LPP) preserves the neighborhood structure of the dataset by finding the optimal linear approximations to the eigenfunctions of the Laplace-Beltrami operator on the manifold, which makes it have several advantages of both linear and nonlinear methods. However, its neighborhood graph is generated by adopting the Euclidean distance as the similarity metric of different samples which leads to the unsatisfying effectiveness of LPP. To address the limitation of Euclidean distance we propose an improved LPP called Manifold Ranking-based LPP (MRLPP) which can effectively preserve the neighborhood structure of the dataset, either globular or non-globular. Experimental results on several datasets demonstrate the effectiveness of our method.
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Wei, J., Chen, Z., Niu, P., Chen, Y., Chen, W. (2011). Manifold Ranking-Based Locality Preserving Projections. In: Deng, H., Miao, D., Lei, J., Wang, F.L. (eds) Artificial Intelligence and Computational Intelligence. AICI 2011. Lecture Notes in Computer Science(), vol 7003. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23887-1_84
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DOI: https://doi.org/10.1007/978-3-642-23887-1_84
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23886-4
Online ISBN: 978-3-642-23887-1
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