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Proceedings Paper

Geometric multi-resolution analysis for dictionary learning
Author(s): Mauro Maggioni; Stanislav Minsker; Nate Strawn
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Paper Abstract

We present an efficient algorithm and theory for Geometric Multi-Resolution Analysis (GMRA), a procedure for dictionary learning. Sparse dictionary learning provides the necessary complexity reduction for the critical applications of compression, regression, and classification in high-dimensional data analysis. As such, it is a critical technique in data science and it is important to have techniques that admit both efficient implementation and strong theory for large classes of theoretical models. By construction, GMRA is computationally efficient and in this paper we describe how the GMRA correctly approximates a large class of plausible models (namely, the noisy manifolds).

Paper Details

Date Published: 11 September 2015
PDF: 9 pages
Proc. SPIE 9597, Wavelets and Sparsity XVI, 95971C (11 September 2015); doi: 10.1117/12.2189594
Show Author Affiliations
Mauro Maggioni, Duke Univ. (United States)
Stanislav Minsker, Univ. Southern California (United States)
Nate Strawn, Georgetown Univ. (United States)


Published in SPIE Proceedings Vol. 9597:
Wavelets and Sparsity XVI
Manos Papadakis; Vivek K. Goyal; Dimitri Van De Ville, Editor(s)

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