Regularizing the Local Similarity Discriminant Analysis Classifier

Abstract--We investigate parameter-based and distributionbased approaches to regularizing the generative, similarity-based classifier called local similarity discriminant analysis classifier (local SDA). We argue that regularizing distributions rather than parameters can both increase the model flexibility and decrease estimation variance while retaining the conceptual underpinnings of the local SDA classifier. Experiments with four benchmark similarity-based classification datasets show that the proposed regularization significantly improves classification performance compared to the local SDA classifier, and the distributionbased approach improves performance more consistently than the parameter-based approaches. Also, regularized local SDA can perform significantly better than similarity-based SVM classifiers, particularly on sparse and highly nonmetric similarities. Keywords-local similarity discriminant analysis; regularized local similarity discriminant analysis; I. SIMILARITY-BA...

• Type: Journal
• Year: 2009
• Where: ICMLA
• Authors: Luca Cazzanti, Maya R. Gupta
• Keywords: ICMLA 2009, Local Sda, Machine Learning, Similarity Discriminant Analysis, Similarity-based Classifiers