Skip to main content

Research Repository

Advanced Search

Deep Regularized Discriminative Network

Mandal, Bappaditya

Deep Regularized Discriminative Network Thumbnail



Traditional linear discriminant analysis (LDA) approach discards the eigenvalues which are very small or equivalent to zero, but quite often eigenvectors corresponding to zero eigenvalues are the important dimensions for discriminant analysis. We propose an objective function which would utilize both the principal as well as nullspace eigenvalues and simultaneously inherit the class separability information onto its latent space representation. The idea is to build a convolutional neural network (CNN) and perform the regularized discriminant analysis on top of this and train it in an end-to-end fashion. The backpropagation is performed with a suitable optimizer to update the parameters so that the whole CNN approach minimizes the within class variance and maximizes the total class variance information suitable for both multi-class and binary class classification problems. Experimental results on four databases for multiple computer vision classification tasks show the efficacy of our proposed approach as compared to other popular methods.


Mandal, B. (2021). Deep Regularized Discriminative Network.

Acceptance Date Apr 15, 2021
Publication Date Apr 24, 2021
Journal SN Computer Science
Print ISSN 2662-995X
Pages 1-9
Public URL
Publisher URL


You might also like

Downloadable Citations