Local similarity discriminant analysis

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Local similarity discriminant analysis
We propose a local, generative model for similarity-based classification. The method is applicable to the case that only pairwise similarities between samples are available. The classifier models the local class-conditional distribution using a maximum entropy estimate and empirical moment constraints. The resulting exponential class conditionaldistributions are combined with class prior probabilities and misclassification costs to form the local similarity discriminant analysis (local SDA) classifier. We compare the performance of local SDA to a non-local version, to the local nearest centroid classifier, the nearest centroid classifier, k-NN, and to the recently-developed potential support vector machine (PSVM). Results show that local SDA is competitive with k-NN and the computationally-demanding PSVM while offering the advantages of a generative classifier.
Luca Cazzanti, Maya R. Gupta
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2007
Where ICML
Authors Luca Cazzanti, Maya R. Gupta
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