Creator:

Zhong, Yiun ; Li, Chongjun ; Li, Zhong ; Duan, Xiaojuan

Contributor:

Yao, Baozhen - ed. ; Wang, Shuaian - ed. ; Asian, Sobhan - ed.

Title:

A proximal-based algorithm for piecewise sparse approximation with application to scattered data fitting

Subtitle:

.

Group publication title:

AMCS, volume 32 (2022)

Subject and Keywords:

piecewise sparse approximation ; proximal gradient ; scattered data fitting

Abstract:

In some applications, there are signals with a piecewise structure to be recovered. In this paper, we propose a piecewise sparse approximation model and a piecewise proximal gradient method (JPGA) which aim to approximate piecewise signals. We also make an analysis of the JPGA based on differential equations, which provides another perspective on the convergence rate of the JPGA. In addition, we show that the problem of sparse representation of the fitting surface to the given scattered data can be considered as a piecewise sparse approximation. Numerical experimental results show that the JPGA can not only effectively fit the surface, but also protect the piecewise sparsity of the representation coefficient.

Publisher:

Zielona Góra: Uniwersytet Zielonogórski

Date:

2022

Resource Type:

artykuł

DOI:

10.34768/amcs-2022-0046

Pages:

671-682

Source:

AMCS, volume 32, number 4 (2022) ; click here to follow the link

Language:

eng

License CC BY 4.0:

click here to follow the link

Rights:

Biblioteka Uniwersytetu Zielonogórskiego