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Patch Alignment for Graph Embedding

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Graph Embedding for Pattern Analysis

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Abstract

Dozens of manifold learning-based dimensionality reduction algorithms have been proposed in the literature. The most representative ones are locally linear embedding (LLE) [65], ISOMAP [76], Laplacian eigenmaps (LE) [4], Hessian eigenmaps (HLLE) [20], and local tangent space alignment (LTSA) [102]. LLE uses linear coefficients, which reconstruct a given example by its neighbors, to represent the local geometry, and then seeks a low-dimensional embedding, in which these coefficients are still suitable for reconstruction. ISOMAP preserves global geodesic distances of all the pairs of examples.

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Authors and Affiliations

  1. Key Laboratory of Machine Perception, Peking University, No.5 Yihe Road, Haidian District, Beijing, 100871, P.R. China

    Yong Luo & Chao Xu

  2. Centre for Quantum Computation and Intelligent Systems, University of Technology, Sydney, Jones Street, Ultimo, NSW 2007, Sydney, Australia

    Dacheng Tao

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  1. Yong Luo
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  2. Dacheng Tao
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  3. Chao Xu
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Correspondence to Dacheng Tao .

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  1. , Dept. of ECE, College of Engineering, Northeastern University, 403 Dana Research Center, 360 Huntington Ave, Boston, 02115, Massachusetts, USA

    Yun Fu

  2. Honeywell, Douglas Drive North 1985, Golden Valley, 55422, USA

    Yunqian Ma

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Luo, Y., Tao, D., Xu, C. (2013). Patch Alignment for Graph Embedding. In: Fu, Y., Ma, Y. (eds) Graph Embedding for Pattern Analysis. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-4457-2_4

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