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K/K-Nearest Neighborhood criterion for improving locally linear embedding

Eftekhari, A ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1109/CGIV.2009.81
  3. Abstract:
  4. Spectral manifold learning techniques have recently found extensive applications in machine vision. The common strategy of spectral algorithms for manifold learning is exploiting the local relationships in a symmetric adjacency graph, which is typically constructed using k-nearest neighborhood (k-NN) criterion. In this paper, with our focus on locally linear embedding as a powerful and well-known spectral technique, shortcomings of k-NN for construction of the adjacency graph are first illustrated, and then a new criterion, namely k/K-nearest neighborhood (k/K-NN) is introduced to overcome these drawbacks. The proposed criterion involves finding the sparsest representation of each sample in the dataset, and is realized by modifying Robust-SL0, a recently proposed algorithm for sparse approximate representation. k/K-NN criterion gives rise to a modified spectral manifold learning technique, namely Sparse-LLE, which demonstrates remarkable improvement over conventional LLE through our experiments. © 2009 IEEE
  5. Keywords:
  6. Robust-sl ; Adjacency graphs ; Common strategy ; Data sets ; K-nearest neighborhoods ; Locally linear embedding ; Machine vision ; Manifold learning ; Nearest neighborhood ; Sparse representation ; Spectral algorithm ; Spectral techniques ; Computer graphics ; Computer vision ; Visualization ; Learning algorithms
  7. Source: Proceedings of the 2009 6th International Conference on Computer Graphics, Imaging and Visualization: New Advances and Trends, CGIV2009, 11 August 2009 through 14 August 2009, Tianjin ; 2009 , Pages 392-397 ; 9780769537894 (ISBN)
  8. URL: http://ieeexplore.ieee.org/document/5298792/?reload=true