Human face recognition have been an active research area for the last few decades.
Especially, during the last five years, it has gained significant research attention from …

Feature selection is a significant task in the workflow of predictive modeling for data
analysis. Recent advanced feature selection methods are using the power of optimization …

Existing block-diagonal representation studies mainly focuses on casting block-diagonal
regularization on training data, while only little attention is dedicated to concurrently learning …

The existing occlusion face recognition algorithms almost tend to pay more attention to the
visible facial components. However, these models are limited because they heavily rely on …

State-of-the-art palmprint recognition methods have achieved significant performances.
However, most of the existing methods are focused on particular scenarios such as a …

Sparse Bayesian learning (SBL) is a popular machine learning approach with a superior
generalization capability due to the sparsity of its adopted model. However, it entails a matrix …

Image classification is a fundamental component in modern computer vision systems, where
sparse representation-based classification has drawn a lot of attention due to its robustness …

Abstract Broad Learning System (BLS) is an emerging network paradigm that has received
considerable attention in the regression and classification fields. However, there are two …

Learning discriminative feature representations has shown remarkable importance due to its
promising performance for machine learning problems. This paper presents a discriminative …

Class imbalance problems pose significant challenges in the field of data mining. The
skewed distribution of classes in imbalanced datasets often leads conventional classification …