Ridge regression (RR) for classification is a regularized least square method to model the
linear dependency between covariate variables and labels. By applying appropriate …

In this paper, we present novel ridge regression (RR) and kernel ridge regression (KRR)
techniques for multivariate labels and apply the methods to the problem efface recognition …

Linear regression classification (LRC) has attracted a great amount of attention owning to its
promising performance in face recognition. However, its performance will dramatically …

In the last decade, many variants of classical linear discriminant analysis (LDA) have been
developed to tackle the under-sampled problem in face recognition. However, choosing the …

To improve the robustness of the linear regression classification (LRC) algorithm, in this
paper, we propose a linear discriminant regression classification (LDRC) algorithm to boost …

In this paper, a novel algorithm named Semi-supervised Local Ridge Regression (SSLRR)
is proposed for local matching based face recognition. Compared with other algorithms, the …

In this letter, we propose a unitary regression classification (URC) algorithm, which could
achieve total minimum projection error, to improve the robustness of face recognition …

Linear regression technique is an efficient method to solve face recognition problem. It's
based on the theory that images in the same class will also belong to same linear subspace …

Ridge Regression (RR) is a classical method that is widely used in multiple regression
analysis. However, traditional RR does not take the local geometric structure of data into …

On face recognition, most previous works on dimensionality reduction and classification
would first transform the input image into 1-D vector, which ignores the underlying data …