Deep dictionary learning (DDL) can mine deeper representations of data more effectively
than single-layer dictionary learning. However, existing DDL methods with specific sparse …

Due to the ability to prevent over-fitting, reduce computational complexity and storage cost,
and enhance interpretability, unsupervised feature selection (UFS) has received widespread …

The hyperspectral image super-resolution (HSI-SR) problem aims at reconstructing the high
resolution spatial–spectral information of the scene by fusing low-resolution hyperspectral …

Broad Learning System (BLS) has been proven to be one of the most important techniques
for classification and regression in machine learning and data mining. BLS directly collects …

The multispectral and hyperspectral image fusion (MHF) technique is designed to address
the challenge of integrating the spatiotemporal characteristics of multispectral images with …

对于稀疏角度下的投影数据, 计算机断层扫描在图像重建中容易出现伪影和噪声较多的问题,
难以满足工业及医学诊断要求. 本文提出一种基于重叠组稀疏和超拉普拉斯先验的稀疏角度CT …

Spectral computed tomography (CT) based on photon-counting detectors, has great
potential in material decomposition, tissue characterization, lesion detection, and other …

基于光子计数探测器的能谱CT 在材料分解, 组织表征, 病变检测等应用中具有巨大的潜力.
在重建过程中, 通道数的增加会造成单通道中光子数减少, 从而导致重建图像质量下降 …

Clustering refers to the problem of partitioning data into several groups according to the
predefined criterion. Graph-based method is one of main clustering approaches and has …

Emotional state recognition is an important part of emotional research. Compared to non-
physiological signals, the electroencephalogram (EEG) signals can truly and objectively …