Sparse shift-invariant NMF

Vamsi K. Potluru, Sergey M. Plis, Vince D. Calhoun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Non-negative Matrix Factorization (NMF) has increasingly been used for efficiently decomposing multivariate data into a signal dictionary and corresponding activations. In this paper, we propose an algorithm called sparse shift-invariant NMF (ssiNMF) for learning possibly overcomplete shift-invariant features. This is done by incorporating a circulant property on the features and sparsity constraints on the activations. The circulant property allows us to capture shifts in the features and enables efficient computation by the Fast Fourier Transform. The ssiNMF algorithm turns out to be matrix-free for we need to store only a small number of features. We demonstrate this on a dataset generated from an overcomplete set of bars.

Original languageEnglish (US)
Title of host publication2008 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2008 - Proceedings
Pages69-72
Number of pages4
DOIs
StatePublished - Dec 1 2008
Externally publishedYes
Event2008 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2008 - Santa Fe, NM, United States
Duration: Mar 24 2008Mar 26 2008

Publication series

NameProceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation

Other

Other2008 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2008
CountryUnited States
CitySanta Fe, NM
Period3/24/083/26/08

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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  • Cite this

    Potluru, V. K., Plis, S. M., & Calhoun, V. D. (2008). Sparse shift-invariant NMF. In 2008 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2008 - Proceedings (pp. 69-72). [4512287] (Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation). https://doi.org/10.1109/SSIAI.2008.4512287