As basic research, it has also received increasing attention from people that the “curse of
dimensionality” will lead to increase the cost of data storage and computing; it also …

Sparse estimation methods are aimed at using or obtaining parsimonious representations of
data or models. They were first dedicated to linear variable selection but numerous …

In recent years, a large amount of multi-disciplinary research has been conducted on sparse
models and their applications. In statistics and machine learning, the sparsity principle is …

We address the problem of finding sparse solutions to an underdetermined system of
equations when there are multiple measurement vectors having the same, but unknown …

The K-SVD algorithm is a highly effective method of training overcomplete dictionaries for
sparse signal representation. In this report we discuss an efficient implementation of this …

Recent state-of-the-art image denoising methods use nonparametric estimation processes
for 8*8 patches and obtain surprisingly good denoising results. The mathematical and …

Array processing is widely used in sensing applications for estimating the locations and
waveforms of the sources in a given field. In the absence of a large number of snapshots …

Recently, Wi-Fi-based human activity recognition using channel state information (CSI)
signals has gained popularity due to its potential features, such as passive sensing and …

An efficient and flexible dictionary structure is proposed for sparse and redundant signal
representation. The proposed sparse dictionary is based on a sparsity model of the …

Channels with a sparse impulse response arise in a number of communication applications.
Exploiting the sparsity of the channel, we show how an estimate of the channel may be …