Probabilistic Class-Specific Discriminant Analysis
This work addresses a specific issue in machine learning for pattern recognition, but it appears incremental as it extends existing class-specific discriminant analysis methods into a probabilistic framework.
The paper tackles the problem of class-specific discriminant subspace learning by formulating a probabilistic model that incorporates the multi-modal structure of the negative class, which existing methods neglect, and shows that it can be used for class-specific probabilistic classification, with performance illustrated in verification and classification problems.
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.