Local binary patterns (LBP) are one of the most important image representations. However,
LBPs have not been as successful as other methods in palmprint recognition. Originally, the …

This paper presents an effective scene classification approach based on collaborative
representation fusion of local and global spatial features. First, a visual word codebook is …

This paper presents a novel reconfigurable framework for training Convolutional Neural
Networks (CNNs). The proposed framework is based on reconfiguring a streaming datapath …

Recent works have proposed two L1-norm distance measure-based linear discriminant
analysis (LDA) methods, L1-LD and LDA-L1, which aim to promote the robustness of the …

O-rings are one of the most common seals used in industry. Precision measurement and
inspection of O-rings play a vital role in seal quality control. Human inspection is a traditional …

Principal component analysis (PCA) algorithm has been extensively employed in face
recognition. However, existing PCA algorithms have some limitations in face recognition. In …

Presenting a clear bridge between theory and application, this volume provides a thorough
description of the mechanism of support vector machines (SVMs) from the point of view of …

Collaborative graph-based discriminant analysis (CGDA) has been recently proposed for
dimensionality reduction and classification of hyperspectral imagery, offering superior …

We proposed a hybrid algorithm by combining kernel entropy component analysis (KECA)
with linear discriminant analysis (LDA), namely, KECA-LDA for feature reduction in …

Feature selection, selecting the most informative subset of features, is an important research
direction in dimension reduction. The combinatorial search in feature selection is essentially …