Generative models for similarity-based classification

Authors:
Luca Cazzanti;Maya R. Gupta;Anjali J. Koppal
Affiliations:
Applied Physics Lab, Seattle, WA, USA;University of Washington, Seattle, WA, USA;University of California, Berkeley, CA, USA
Venue:
Pattern Recognition
Year:
2008

Citing 22
Cited 5

Elements of information theory

Elements of information theory
A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features

Machine Learning
Similarity Measures

IEEE Transactions on Pattern Analysis and Machine Intelligence
Relational discriminant analysis

Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Classification on pairwise proximity data

Proceedings of the 1998 conference on Advances in neural information processing systems II
Classification in non-metric spaces

Proceedings of the 1998 conference on Advances in neural information processing systems II
Classification with Nonmetric Distances: Image Retrieval and Class Representation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Discovering Useful Concept Prototypes for Classification Based on Filtering and Abstraction

IEEE Transactions on Pattern Analysis and Machine Intelligence
Pairwise Data Clustering by Deterministic Annealing

IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts

IEEE Transactions on Pattern Analysis and Machine Intelligence
An Information-Theoretic Definition of Similarity

ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Efficient Pattern Recognition Using a New Transformation Distance

Advances in Neural Information Processing Systems 5, [NIPS Conference]
A generalized kernel approach to dissimilarity-based classification

The Journal of Machine Learning Research
Noun classification from predicate-argument structures

ACL '90 Proceedings of the 28th annual meeting on Association for Computational Linguistics
Support vector machines for dyadic data

Neural Computation
On the information and representation of non-Euclidean pairwise data

Pattern Recognition
Experimental study on prototype optimisation algorithms for prototype-based classification in vector spaces

Pattern Recognition
Editorial: Similarity-based pattern recognition

Pattern Recognition
Prototype selection for dissimilarity-based classifiers

Pattern Recognition
Using information content to evaluate semantic similarity in a taxonomy

IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Information distance

IEEE Transactions on Information Theory
The similarity metric

IEEE Transactions on Information Theory

Object classification by fusing SVMs and Gaussian mixtures

Pattern Recognition
Bayesian hybrid generative discriminative learning based on finite Liouville mixture models

Pattern Recognition
Multi-task regularization of generative similarity models

SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition
Learning from pairwise constraints by Similarity Neural Networks

Neural Networks
Fast exact local-mean based classification algorithm using class rejection and initial distance assigning processes

Pattern Recognition Letters

Quantified Score

Hi-index	0.01

Visualization

Abstract

A maximum-entropy approach to generative similarity-based classifiers model is proposed. First, a descriptive set of similarity statistics is assumed to be sufficient for classification. Then the class-conditional distributions of these descriptive statistics are estimated as the maximum-entropy distributions subject to empirical moment constraints. The resulting exponential class-conditional distributions are used in a maximum a posteriori decision rule, forming the similarity discriminant analysis (SDA) classifier. Simulated and real data experiments compare performance to the k-nearest neighbor classifier, the nearest-centroid classifier, and the potential support vector machine (PSVM).