While null space based linear discriminant analysis (NLDA) obtains a good discriminant performance, the ability easily suffers from an implicit assumption of Gaussian model with same covariance each class. Meanwhile, mixture model discriminant analysis, which is a good way for processing issues on multiple subclasses in each class, depends on human experience on the number of subclasses and has...

In this work we utilize a supervised acoustic model training pipeline without supervision to improve Dirichlet process Gaussian mixture model (DPGMM) based feature vector clustering. We exploit methods common in supervised acoustic modeling to unsupervisedly learn feature transformations for application to the input data prior to clustering. The idea is to automatically find mappings of feature...

Quadratic discriminant analysis (QDA) is a widely used statistical tool to classify observations from different multivariate Normal populations. The generalized quadratic (GQDA) classification rule/classifier, which generalizes the QDA and minimum Mahalanobis distance (MMD) classifiers discriminate between populations with underlying elliptically symmetric distributions competes quite favorably...

Summary Functional linear discriminant analysis provides a simple yet efficient method for classification, with the possibility of achieving perfect classification. Several methods have been proposed in literature that mostly address dimensionality problem. On other hand, there is growing interest interpretability analysis, which favours and sparse solution. In this paper we propose new approac...

In this paper, we present a classi®cation model to evaluate the performance of companies on the basis of qualitative criteria, such as organizational and managerial variables. The classi®cation model evaluates the eligibility of the company to receive state subsidies for the development of high tech products. We furthermore created a similar model using the backpropagation learning algorithm an...