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Generalized Dictionary Learning for Symmetric Positive Definite Matrices with Application to Nearest Neighbor Retrieval

  • Suvrit Sra
  • Anoop Cherian
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6913)

Abstract

We introduce Generalized Dictionary Learning (GDL), a simple but practical framework for learning dictionaries over the manifold of positive definite matrices. We illustrate GDL by applying it to Nearest Neighbor (NN) retrieval, a task of fundamental importance in disciplines such as machine learning and computer vision. GDL distinguishes itself from traditional dictionary learning approaches by explicitly taking into account the manifold structure of the data. In particular, GDL allows performing “sparse coding” of positive definite matrices, which enables better NN retrieval. Experiments on several covariance matrix datasets show that GDL achieves performance rivaling state-of-the-art techniques.

Keywords

Face Recognition Covariance Matrice Near Neighbor Online Algorithm Geodesic Distance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Suvrit Sra
    • 1
  • Anoop Cherian
    • 2
  1. 1.MPI for Intelligent SystemsTübingenGermany
  2. 2.University of MinnesotaTwin CitiesUSA

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