Sparse Coding on Symmetric Positive Definite Manifolds Using Bregman Divergences

This paper introduces sparse coding and dictionary learning for symmetric positive definite (SPD) matrices, which are often used in machine learning, computer vision, and related areas. Unlike traditional sparse coding schemes that work in vector spaces, in this paper, we discuss how SPD matrices ca...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems 2016-06, Vol.27 (6), p.1294-1306
Hauptverfasser:	Harandi, Mehrtash T., Hartley, Richard, Lovell, Brian, Sanderson, Conrad
Format:	Artikel
Sprache:	eng
Schlagworte:	Bregman's divergences Classification Coding Computer vision Dictionaries dictionary learning Encoding Kernel kernel methods Learning Manifolds Riemannian's geometry sparse coding Sparse matrices Symmetric matrices Symmetry Tasks Texture Tin
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