Learning spatially localized, parts-based representation
Top Cited Papers
- 25 August 2005
- proceedings article
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 1, 207
- https://doi.org/10.1109/cvpr.2001.990477
Abstract
In this paper, we propose a novel method, called local non- negative matrix factorization (LNMF), for learning spa- tially localized, parts-based subspace representation of vi- sual patterns. An objective function is defined to impose lo- calization constraint, in addition to the non-negativity con- straint in the standard NMF (1). This gives a set of bases which not only allows a non-subtractive (part-based) repre- sentation of images but also manifests localized features. An algorithm is presented for the learning of such basis components. Experimental results are presented to compare LNMF with the NMF and PCA methods for face represen- tation and recognition, which demonstrates advantages of LNMF.Keywords
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