pLSA for sparse arrays with tsallis pseudo-additive divergence: noise robustness and algorithm

Tamir Hazan*, Roee Hardoon, Amnon Shashua

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

5 Scopus citations

Abstract

We introduce the Tsallis divergence error measure in the context of pLSA matrix and tensor decompositions showing much improved performance in the presence of noise. The focus of our approach is on one hand to provide an optimization framework which extends (in the sense of a one parameter family) the Maximum Likelihood framework and on the other hand is theoretically guaranteed to provide robustness under clutter, noise and outliers in the measurement matrix under certain conditions. Specifically, the conditions under which our approach excels is when the measurement array (co-occurrences) is sparse - which happens in the application domain of "bag of visual words".

Original languageAmerican English
DOIs
StatePublished - 2007
Event2007 IEEE 11th International Conference on Computer Vision, ICCV - Rio de Janeiro, Brazil
Duration: 14 Oct 200721 Oct 2007

Conference

Conference2007 IEEE 11th International Conference on Computer Vision, ICCV
Country/TerritoryBrazil
CityRio de Janeiro
Period14/10/0721/10/07

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