Sökning: id:"swepub:oai:DiVA.org:his-21403" >
Effects of network ...
Effects of network topology on the performance of consensus and distributed learning of SVMs using ADMM
-
- Tavara, Shirin (författare)
- Data Science and AI division, Department of Computer Science and Engineering, Chalmers University of Technology and University of Gothenburg, Gothenburg, Sweden,Skövde Artificial Intelligence Lab (SAIL),Chalmers tekniska högskola,Chalmers University of Technology
-
- Schliep, Alexander, 1967 (författare)
- Gothenburg University,Göteborgs universitet,Institutionen för data- och informationsteknik (GU),Department of Computer Science and Engineering (GU),Data Science and AI division, Department of Computer Science and Engineering, Chalmers University of Technology and University of Gothenburg, Gothenburg, Sweden
-
(creator_code:org_t)
- 2021-03-09
- 2021
- Engelska.
-
Ingår i: PeerJ Computer Science. - : PeerJ. - 2376-5992. ; 7
- Relaterad länk:
-
https://doi.org/10.7...
-
visa fler...
-
https://his.diva-por... (primary) (Raw object)
-
https://doi.org/10.7...
-
https://research.cha... (primary) (free)
-
https://urn.kb.se/re...
-
https://doi.org/10.7...
-
https://research.cha...
-
https://gup.ub.gu.se...
-
visa färre...
Abstract
Ämnesord
Stäng
- The Alternating Direction Method of Multipliers (ADMM) is a popular and promising distributed framework for solving large-scale machine learning problems. We consider decentralized consensus-based ADMM in which nodes may only communicate with one-hop neighbors. This may cause slow convergence. We investigate the impact of network topology on the performance of an ADMM-based learning of Support Vector Machine using expander, and mean-degree graphs, and additionally some of the common modern network topologies. In particular, we investigate to which degree the expansion property of the network influences the convergence in terms of iterations, training and communication time. We furthermore suggest which topology is preferable. Additionally, we provide an implementation that makes these theoretical advances easily available. The results show that the performance of decentralized ADMM-based learning of SVMs in terms of convergence is improved using graphs with large spectral gaps, higher and homogeneous degrees.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Telekommunikation (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Telecommunications (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences (hsv//eng)
Nyckelord
- Data Mining and Machine Learning
- Distributed and Parallel Computing
- Network Science and Online Social Networks
- Machine learning
- Parallel and distributed computing
- SVMs
- ADMM
- Expander graphs
- Distributed optimization
- Convergence
- Machine learning
- Parallel and distributed computing
- SVMs
- ADMM
- Expander graphs
- Distributed optimization
- Convergence
- alternating direction method
- Computer Science
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
Hitta via bibliotek
Till lärosätets databas