# Probabilistic Class-Specific Discriminant Analysis

## TL;DR

This paper introduces a probabilistic model for class-specific discriminant analysis that captures multi-modal negative class structures and extends existing methods into probabilistic classifiers, improving verification and classification tasks.

## Contribution

It formulates a novel probabilistic framework for class-specific discriminant analysis, accommodating multi-modal negative classes and enabling probabilistic classification rules.

## Key findings

- Model captures multi-modal negative class structure.
- Existing methods are special cases of the proposed model.
- Improved performance in verification and classification tasks.

## Abstract

In this paper we formulate a probabilistic model for class-specific discriminant subspace learning. The proposed model can naturally incorporate the multi-modal structure of the negative class, which is neglected by existing class-specific methods. Moreover, it can be directly used to define a class-specific probabilistic classification rule in the discriminant subspace. We show that existing class-specific discriminant analysis methods are special cases of the proposed probabilistic model and, by casting them as probabilistic models, they can be extended to class-specific classifiers. We illustrate the performance of the proposed model in both verification and classification problems.