摘要稀疏表示模型常利用训练样本学习过完备字典, 旨在获得信号的冗余稀疏表示. 设计简单,
高效, 通用性强的字典学习算法是目前的主要研究方向之一, 也是信息领域的研究热点 …

Objective: Improve the reconstructed image with fast and multiclass dictionaries learning
when magnetic resonance imaging is accelerated by undersampling the k-space data …

This book revolves around the question of designing a matrix D∈ Rm× n called dictionary,
such that to obtain good sparse representations y≈ Dx for a class of signals y∈ Rm given …

A dictionary learning algorithm learns a set of atoms from some training signals in such a
way that each signal can be approximated as a linear combination of only a few atoms. Most …

An enhanced sequential Monte Carlo probability hypothesis density (PHD) filter-based
multiple human tracking system is presented. The proposed system mainly exploits two …

A dictionary learning problem is a matrix factorization in which the goal is to factorize a
training data matrix, Y, as the product of a dictionary, D, and a sparse coefficient matrix, X, as …

The rotation, vibration, or coning motion of a target may produce periodic Doppler
modulation, which is called the micro-Doppler phenomenon and is widely used for target …

Compressive Sensing (CS) allows to acquire signals at sampling rates significantly lower
than the Nyquist rate, provided that the signals possess a sparse representation in an …

Discriminative dictionary learning (DDL) aims to address pattern classification problems via
learning dictionaries from training samples. Dictionary pair learning (DPL) based DDL has …

In this paper, a robust sparse representation for medical image classification is proposed
based on the adaptive type-2 fuzzy learning (T2-FDL) system. In the proposed method …