Representative Selection with Structured Sparsity
We propose a novel formulation to find representatives in data samples via learning with structured sparsity. To find representatives with both diversity and representativeness, we formulate the problem as a structurally-regularized learning where the objective function consists of a reconstruction...
Saved in:
Main Authors: | Wang, Hongxing, Kawahara, Yoshinobu, Weng, Chaoqun, Yuan, Junsong |
---|---|
Other Authors: | School of Electrical and Electronic Engineering |
Format: | Article |
Language: | English |
Published: |
2017
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/82103 http://hdl.handle.net/10220/43501 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Sparsity Analysis for Computer Vision Applications
by: CHENG BIN
Published: (2013) -
Annotating web images using NOVA: NOn-conVex group spArsity
by: Wu, F., et al.
Published: (2014) -
Sparse sensor selection for distributed systems: an l1-relaxation approach
by: Zhong, Yuxing, et al.
Published: (2024) -
SPARSITY BASED REGULARIZATION FOR SIGNAL RECOVERY AND CLUSTERING
by: XU GUODONG
Published: (2018) -
Array-based underwater acoustic target classification with spectrum reconstruction based on joint sparsity and frequency shift invariant feature
by: Lu, Chenxiang, et al.
Published: (2023)