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K/K-nearest neighborhood criterion for improvement of locally linear embedding

Eftekhari, A ; Sharif University of Technology | 2009

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  1. Type of Document: Article
  2. DOI: 10.1007/978-3-642-03767-2_98
  3. Publisher: 2009
  4. Abstract:
  5. 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 Springer Berlin Heidelberg
  6. Keywords:
  7. Adjacency graphs ; Common strategy ; Data sets ; K-nearest neighborhoods ; Local linear embedding ; Locally linear embedding ; Machine vision ; Manifold learning ; Nearest neighborhood ; Robust-SL0 ; Sparse representation ; Spectral algorithm ; Spectral techniques ; Computer vision ; Image analysis ; Learning algorithms
  8. Source: 13th International Conference on Computer Analysis of Images and Patterns, CAIP 2009, Munster, 2 September 2009 through 4 September 2009 ; Volume 5702 LNCS , 2009 , Pages 808-815 ; 03029743 (ISSN); 3642037666 (ISBN); 9783642037665 (ISBN)
  9. URL: https://link.springer.com/chapter/10.1007/978-3-642-03767-2_98