Patch Alignment for Graph Embedding

Chapter

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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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Key Laboratory of Machine PerceptionPeking UniversityBeijingP.R. China
  2. 2.Centre for Quantum Computation and Intelligent SystemsUniversity of Technology, SydneyUltimoAustralia

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