We introduce Deep Linear Discriminant Analysis (DeepLDA) which learns linearly separable latent representations in an end-to-end fashion. Classic LDA extracts features which preserve class separability and is used for dimensionality reduction for many classification problems. The central idea of this paper is to put LDA on top of a deep neural network. This can be seen as a non-linear extension...

Most wholesalers are small family-owned firms that frequently lack marketing skills and an infrastructure capable of setting into practice state-of-the-art marketing distribution systems. They are much more consumed with daily activities and credit and collection functions than they are with developing marketing skills and logistics networks. The purpose of this exploratory study is to identify...

This paper proposes a DEA-based approach to credit scoring. Compared with conventional models such as multiple discriminant analysis, logistic regression analysis, and neural networks for business failure prediction, which require extra a priori information, this new approach solely requires ex-post information to calculate credit scores. For the empirical evidence, this methodology was applied...

Discriminant analysis is a technique for the multivariate study of group differences. More specifically, it provides a method of examining the extent to which multiple predictor variables are related to a categorical criterion, that is, group membership. Situations in which the technique is particularly useful include those in which the researcher wishes to assess which of a number of continuou...

Linear discriminant analysis introduced by Fisher is a known dimension reduction and classification approach that has received much attention in the statistical literature. Most researchers have focused attention on its deficiencies. As such different versions of classification procedures have been introduced for various applications. In this paper, we attempt not to robustify the Fisher linear...