Skip to main navigation
Skip to search
Skip to main content
Experts@Minnesota Home
Home
Profiles
Research units
University Assets
Projects and Grants
Research output
Datasets
Press/Media
Activities
Fellowships, Honors, and Prizes
Impacts
Search by expertise, name or affiliation
New operators for fixed-point theory: The sparsity-aware learning case
Konstantinos Slavakis
, Yannis Kopsinis
, Sergios Theodoridis
Digital Technology Center
Research output
:
Chapter in Book/Report/Conference proceeding
›
Conference contribution
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'New operators for fixed-point theory: The sparsity-aware learning case'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
Generalized Thresholding
100%
Fixed Point Theory
100%
New Operator
100%
Sparsity Learning
100%
Nonconvex
50%
Adaptive Algorithm
50%
Recent Advances
50%
Competitive Performance
50%
Operator Family
50%
Novel Family
50%
Time-adaptive
50%
Linear Complexity
50%
Sparsity-aware
50%
Threshold Rule
50%
Hard Thresholding
50%
Quasi-nonexpansive Mapping
50%
Sparsity-based Algorithm
50%
Online Time
50%
Computer Science
Sparsity
100%
Fixed Points
100%
Adaptive Algorithm
33%
Hard Thresholding
33%
Mathematics
Fixed Point Theory
100%
Thresholding
100%
Fixed Points
20%
Linear Complexity
20%
Quasi-Nonexpansive Mapping
20%