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Abstract: In order to perform object recognition it is necessary to learn representations of the underlying components of images. Such components correspond to objects, object-parts, or features. Nonnegative matrix factorisation is a generative model that has been specifically proposed for finding such meaningful representations of image data, through the use of non-negativity constraints on the factors. This article reports on an empirical investigation of the performance of non-negative matrix... (Update)
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BibTeX entry: (Update)
@misc{ michael-journal,
author = "Michael Spratling Michael",
title = "Journal of Machine Learning Research 7 (2006) 793--815 Submitted 6/05;
Revised 11/05; Published 5/06 Learning Image Components for Object Recognition",
url = "citeseer.ist.psu.edu/760861.html" }
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1 Exploring the functional significance of dendritic inhibitio.. - Spratling, Johnson - 2003
1 Non-negative matrix factorization based methods for object r.. (context) - Liu, Zheng - 2004
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1 Local non-negative matrix factorization as a visual represen.. (context) - Feng, Li et al. - 2002
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