Author name cluster

Luca Cazzanti

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers

2 author rows

JMLR Journal 2009 Journal Article

Similarity-based Classification: Concepts and Algorithms

Yihua Chen
Eric K. Garcia
Maya R. Gupta
Ali Rahimi
Luca Cazzanti

This paper reviews and extends the field of similarity-based classification, presenting new analyses, algorithms, data sets, and a comprehensive set of experimental results for a rich collection of classification problems. Specifically, the generalizability of using similarities as features is analyzed, design goals and methods for weighting nearest-neighbors for similarity-based learning are proposed, and different methods for consistently converting similarities into kernels are compared. Experiments on eight real data sets compare eight approaches and their variants to similarity-based learning. [abs] [ pdf ][ bib ] &copy JMLR 2009. ( edit, beta )

PDF Details

ICML Conference 2007 Conference Paper

Local similarity discriminant analysis

Luca Cazzanti
Maya R. Gupta

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 conditional-distributions 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.

Details