An efficient method for computing orthogonal discriminant vectors

TL;DR: This talk shall explore various projection methods for dimension reduction - a prelude to visualization of vectorial and non-vectorial data, and extend PCA/DCA to their kernel variants.

TL;DR: The theoretical analysis shows that an individual's probe image captured with a new pose can be represented by a linear combination of his/her gallery images, and the proposed method achieves higher accuracy on face recognition and verification than the existing techniques.

TL;DR: This paper describes how to turn an LDA into either a neural layer or a classification layer and investigates the effectiveness of LDA-based initialization for the task of layout analysis at pixel level and shows that it outperforms state-of-the-art random weight initialization methods.

TL;DR: In this article, a linear discriminant analysis (LDA) based layer-wise weight initialization method is proposed for deep neural networks, which can be used to turn an LDA into either a neural layer or a classification layer.

TL;DR: A face recognition algorithm which is insensitive to large variation in lighting direction and facial expression is developed, based on Fisher's linear discriminant and produces well separated classes in a low-dimensional subspace, even under severe variations in lighting and facial expressions.

TL;DR: A face recognition algorithm which is insensitive to gross variation in lighting direction and facial expression is developed and the proposed “Fisherface” method has error rates that are significantly lower than those of the Eigenface technique when tested on the same database.

TL;DR: In this article, a non-linear classification technique based on Fisher's discriminant is proposed and the main ingredient is the kernel trick which allows the efficient computation of Fisher discriminant in feature space.