We are researching algorithms for classification and regression given only similarities between samples, and investigating general definitions of similarity for statistical learning. Please see our repository idl.ee.washington/SimilarityLearning for datasets, code, and more papers.

Publications:

"Similarity-based Classification: Concepts and Algorithms," Yihua Chen, Eric K. Garcia, Maya R. Gupta, Luca Cazzanti, and Ali Rahimi, Journal of Machine Learning Research, 2009.

"Generative Models for Similarity-based Classification," Luca Cazzanti, Maya R. Gupta, and Anjali Koppal, Pattern Recognition , vol. 41, no. 7, 2289-2297, 2008.

"Local Similarity Discriminant Analysis," Luca Cazzanti and Maya R. Gupta, Intl. Conf. Machine Learning (ICML), 2007.

"Maximum Entropy Generative Models for Similarity-based Learning," Maya R. Gupta, Luca Cazzanti and Anjali Koppal, Proc. of the IEEE Intl. Symposium on Information Theory, 2007.

"Thesis: Generative Models for Similarity-based Classification," Luca Cazzanti, Univ. of Washington PhD Thesis, 2007.

"Information-theoretic and set-theoretic similarity," Luca Cazzanti and Maya R. Gupta, Proc. of the IEEE Intl. Symposium on Information Theory, 2006.