Spectrometers are key instruments in diverse fields, notably in medical and biosensing
applications. Recent advancements in nanophotonics and computational techniques have …

Both interclass variances and intraclass similarities are crucial for improving the
classification performance of discriminative dictionary learning (DDL) algorithms. However …

High-dimensional data are often treated as collections of data samples approximately drawn
from a union of multiple low-dimensional subspaces. Subspace clustering, where high …

Abstract Domain generalization typically requires data from multiple source domains for
model learning. However, such strong assumption may not always hold in practice …

In this article, we propose a structured robust adaptive dictionary pair learning (RA-DPL)
framework for the discriminative sparse representation (SR) learning. To achieve powerful …

Dictionary learning has emerged as an effective approach for data-driven fault diagnosis
due to its strong sparse representation ability. Nevertheless, the gathered vibration signals …

Sparse representation based classification (SRC), nuclear-norm matrix regression (NMR),
and deep learning (DL) have achieved a great success in face recognition (FR). However …

Deep dictionary learning seeks multiple dictionaries at different image scales to capture
complementary coherent characteristics. We propose a method for learning a hierarchy of …

White matter tract segmentation is a pivotal research area that leverages diffusion-weighted
magnetic resonance imaging (dMRI) for the identification and mapping of individual white …

Internet platforms provide new ways for people to share experiences, generating massive
amounts of data related to various real-world concepts. In this paper, we present an event …