Generative Models for Similarity-based Classification

Abstract

This work proposes a generative framework for similarity-based classification: similarity discriminant analysis (SDA). The classifiers in the SDA framework are similarity-based, because they classify based on the pairwise similarities of samples, and they are generative, because they build class-conditional probability models of the pairwise similarities. The problem of estimating the class-conditional similarity probability models is solved by applying the maximum entropy principle, under the constraint that the mean similarities be equal to the average similarities observed in a set of training samples. Thus, the class-conditional distributions in the SDA framework are exponential functions of the similarities. Within the SDA framework, several classi?ers are analyzed in detail: the SDA classi?er, the local SDA classi?er, the nnSDA classi?er, and the mixture SDA classi?er. Their performance is evaluated on simulated and benchmark data sets, and compared to the performance of existing similarity-based classi?ers which are not generative.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2007
Accession Number
ADA498232

Entities

People

  • Luca Cazzanti

Organizations

  • University of Washington

Tags

Communities of Interest

  • C4I
  • Energy and Power Technologies
  • Human Systems

DTIC Thesaurus Topics

  • Bayesian Networks
  • Classification
  • Computational Science
  • Data Sets
  • Databases
  • Discriminant Analysis
  • Generative Models
  • Information Processing
  • Information Science
  • Kernel Functions
  • Machine Learning
  • Models
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Psychology
  • Supervised Machine Learning

Readers

  • Missile Defense Systems.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference