Generative, Fully Bayesian, Gaussian, Openset Pattern Classifier
This work addresses pattern classification with uncertainty handling for unseen classes, but it appears incremental as it builds on existing Bayesian and generative methods without claiming broad SOTA improvements.
The authors developed a fully Bayesian, generative classifier for multiclass openset pattern recognition using multivariate Gaussian likelihoods with conjugate priors, achieving closed-form integration of model parameters and providing a model evidence expression for plugin estimation.
This report works out the details of a closed-form, fully Bayesian, multiclass, openset, generative pattern classifier using multivariate Gaussian likelihoods, with conjugate priors. The generative model has a common within-class covariance, which is proportional to the between-class covariance in the conjugate prior. The scalar proportionality constant is the only plugin parameter. All other model parameters are intergated out in closed form. An expression is given for the model evidence, which can be used to make plugin estimates for the proportionality constant. Pattern recognition is done via the predictive likeihoods of classes for which training data is available, as well as a predicitve likelihood for any as yet unseen class.