The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Learning spatially localized, parts-based representation
Abstract
In this paper, we propose a novel method, called local non-negative matrix factorization (LNMF), for learning spatially localized, parts-based subspace representation of visual patterns. An objective function is defined to impose a localization constraint, in addition to the non-negativity constraint in the standard NMF. This gives a set of bases which not only allows a non-subtractive (part-based) representation of images but also manifests localized features. An algorithm is presented for the learning of such basic components. Experimental results are presented to compare LNMF with the NMF and PCA methods for face representation and recognition, which demonstrates advantages of LNMF.
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Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
Cited by
Convex and Semi-Nonnegative Matrix Factorizations
Ding, C. H. Q., Li, T., & Jordan, M. I. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.277 · Cited by 1,317
Nonnegative Matrix Factorization: An Analytical and Interpretive Tool in Computational Biology
Devarajan, K. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000029 · Cited by 454
Kim, J., He, Y., & Park, H. (2014) · Journal of Global Optimization · doi:10.1007/s10898-013-0035-4 · Cited by 352
Zhi, R., Flierl, M., Ruan, Q., & Kleijn, W. B. (2011) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2010.2044788 · Cited by 260
Comparative evaluation of 3D vs. 2D modality for automatic detection of facial action units
Savran, A., Sankur, B., & Taha Bilge, M. (2012) · Pattern Recognition · doi:10.1016/j.patcog.2011.07.022 · Cited by 111
Metadata
- Authors
- Li, S. Z., Hou, X. W., Zhang, H. J., & Cheng, Q. S.
- Venue
- Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001
- Publisher
- IEEE Comput. Soc
- Published
- 2005-08-24
- Volume
- 1
- Pages
- I-207-I-212
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2136171036
- Cited by
- 776
- References
- 1
- FWCI
- 40.12
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Li, S. Z., Hou, X. W., Zhang, H. J., & Cheng, Q. S. (2005). Learning spatially localized, parts-based representation. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001. https://doi.org/10.1109/cvpr.2001.990477
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
