Discriminative k-metrics
Publication
, Journal Article
Szlam, A; Sapiro, G
Published in: Proceedings of the 26th International Conference On Machine Learning, ICML 2009
December 9, 2009
The k q-flats algorithm is a generalization of the popular k-means algorithm where q dimensional best fit affine sets replace centroids as the cluster prototypes. In this work, a modification of the k q-flats framework for pattern classification is introduced. The basic idea is to replace the original reconstruction only energy, which is optimized to obtain the k affine spaces, by a new energy that incorporates discriminative terms. This way, the actual classification task is introduced as part of the design and optimization. The presentation of the proposed framework is complemented with experimental results, showing that the method is computationally very efficient and gives excellent results on standard supervised learning benchmarks.
Duke Scholars
Published In
Proceedings of the 26th International Conference On Machine Learning, ICML 2009
Publication Date
December 9, 2009
Start / End Page
1009 / 1016
Citation
APA
Chicago
ICMJE
MLA
NLM
Szlam, A., & Sapiro, G. (2009). Discriminative k-metrics. Proceedings of the 26th International Conference On Machine Learning, ICML 2009, 1009–1016.
Szlam, A., and G. Sapiro. “Discriminative k-metrics.” Proceedings of the 26th International Conference On Machine Learning, ICML 2009, December 9, 2009, 1009–16.
Szlam A, Sapiro G. Discriminative k-metrics. Proceedings of the 26th International Conference On Machine Learning, ICML 2009. 2009 Dec 9;1009–16.
Szlam, A., and G. Sapiro. “Discriminative k-metrics.” Proceedings of the 26th International Conference On Machine Learning, ICML 2009, Dec. 2009, pp. 1009–16.
Szlam A, Sapiro G. Discriminative k-metrics. Proceedings of the 26th International Conference On Machine Learning, ICML 2009. 2009 Dec 9;1009–1016.
Published In
Proceedings of the 26th International Conference On Machine Learning, ICML 2009
Publication Date
December 9, 2009
Start / End Page
1009 / 1016