Journal Article

Deep Regularized Discriminative Network

Puhan, N. B., Sultana, Nazneen N., Mandal, Bappaditya

2021

Overview

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.

• Publisher: Springer Singapore,Springer Nature B.V
• Subject: Algorithms / Artificial neural networks / Back propagation networks / Classification / Computer Imaging / Computer Science / Computer Systems Organization and Communication Networks / Computer vision / Data Structures and Information Theory / Deep learning / Discriminant analysis / Eigenvalues / Eigenvectors / Information Systems and Communication Service / Neural networks / Original Research / Pattern Recognition and Graphics / Skin cancer / Software Engineering/Programming and Operating Systems / Vision