Indefinite kernel spectral learning

Siamak Mehrkanoon; Xiaolin Huang; Johan A.K. Suykens

Indefinite kernel spectral learning

Siamak Mehrkanoon, Xiaolin Huang, Johan A.K. Suykens

Advanced Computing Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceeding › Academic › peer-review

Abstract

This paper introduces the indefinite learning in the framework of least squares support vector machines (LS-SVM). Here the analysis of the Multi-Class Semi-Supervised Kernel Spectral Clustering (MSS-KSC) model with indefinite kernels is provided. In indefinite MSS-KSC one finds the solution by solving a linear system of equations in the dual. In addition the use of the propped indefinite model for large scale data using Nyström approximation technique as well as unsupervised learning using Kernel Spectral Clustering are also discussed in the published full paper1. Experimental results on several real-life datasets are given to illustrate the efficiency of the proposed indefinite kernel spectral learning.

Original language	English
Title of host publication	Belgian/Netherlands Artificial Intelligence Conference
Subtitle of host publication	30th Benelux Conference on Artificial Intelligence, BNAIC 2018
Pages	131-132
Number of pages	2
Publication status	Published - 1 Jan 2018
Event	30th Benelux Conference on Artificial Intelligence: BNAIC 2018 - Jheronimus Academy of Data Science (JADS), s-Hertogenbosch, Netherlands Duration: 8 Nov 2018 → 9 Nov 2018 Conference number: 30

Conference

Conference	30th Benelux Conference on Artificial Intelligence
Abbreviated title	BNAIC 2018
Country/Territory	Netherlands
City	s-Hertogenbosch
Period	8/11/18 → 9/11/18

Cite this

@inproceedings{14f63181ee1d480b87a092372a796931,

title = "Indefinite kernel spectral learning",

abstract = "This paper introduces the indefinite learning in the framework of least squares support vector machines (LS-SVM). Here the analysis of the Multi-Class Semi-Supervised Kernel Spectral Clustering (MSS-KSC) model with indefinite kernels is provided. In indefinite MSS-KSC one finds the solution by solving a linear system of equations in the dual. In addition the use of the propped indefinite model for large scale data using Nystr{\"o}m approximation technique as well as unsupervised learning using Kernel Spectral Clustering are also discussed in the published full paper1. Experimental results on several real-life datasets are given to illustrate the efficiency of the proposed indefinite kernel spectral learning.",

author = "Siamak Mehrkanoon and Xiaolin Huang and Suykens, {Johan A.K.}",

note = "Funding Information: Acknowledgments. This work was supported by • The Postdoctoral Fellowship of the Research Foundation-Flanders(FWO: 12Z1318N). • National Natural Science Foundation of China (no. 61603248). • Research Council KUL: CoE PFV/10/002, PhD/Postdoc grants Flemish Government;FWO: projects: G0A4917N, G.088114N. Siamak Mehrkanoon is an assistant professor at Maastricht University, the Netherlands. Xiaolin Huang is an associate professor at Shanghai Jiao Tong University, China. Johan Suykens is a full professor at the KU Leuven, Belgium. Funding Information: Acknowledgments. This work was supported by ? The Postdoctoral Fellowship of the Research Foundation-Flanders(FWO: 12Z1318N). ? National Natural Science Foundation of China (no. 61603248). ? Research Council KUL: CoE PFV/10/002, PhD/Postdoc grants Flemish Government;FWO: projects: G0A4917N, G.088114N. Siamak Mehrkanoon is an assistant professor at Maastricht University, the Netherlands. Xiaolin Huang is an associate professor at Shanghai Jiao Tong University, China. Johan Suykens is a full professor at the KU Leuven, Belgium. Publisher Copyright: {\textcopyright} 2018 University of Groningen. All rights reserved.; 30th Benelux Conference on Artificial Intelligence : BNAIC 2018, BNAIC 2018 ; Conference date: 08-11-2018 Through 09-11-2018",

year = "2018",

month = jan,

day = "1",

language = "English",

pages = "131--132",

booktitle = "Belgian/Netherlands Artificial Intelligence Conference",

}

TY - GEN

T1 - Indefinite kernel spectral learning

AU - Mehrkanoon, Siamak

AU - Huang, Xiaolin

AU - Suykens, Johan A.K.

N1 - Conference code: 30

PY - 2018/1/1

Y1 - 2018/1/1

N2 - This paper introduces the indefinite learning in the framework of least squares support vector machines (LS-SVM). Here the analysis of the Multi-Class Semi-Supervised Kernel Spectral Clustering (MSS-KSC) model with indefinite kernels is provided. In indefinite MSS-KSC one finds the solution by solving a linear system of equations in the dual. In addition the use of the propped indefinite model for large scale data using Nyström approximation technique as well as unsupervised learning using Kernel Spectral Clustering are also discussed in the published full paper1. Experimental results on several real-life datasets are given to illustrate the efficiency of the proposed indefinite kernel spectral learning.

AB - This paper introduces the indefinite learning in the framework of least squares support vector machines (LS-SVM). Here the analysis of the Multi-Class Semi-Supervised Kernel Spectral Clustering (MSS-KSC) model with indefinite kernels is provided. In indefinite MSS-KSC one finds the solution by solving a linear system of equations in the dual. In addition the use of the propped indefinite model for large scale data using Nyström approximation technique as well as unsupervised learning using Kernel Spectral Clustering are also discussed in the published full paper1. Experimental results on several real-life datasets are given to illustrate the efficiency of the proposed indefinite kernel spectral learning.

UR - http://www.scopus.com/inward/record.url?scp=85072679782&partnerID=8YFLogxK

M3 - Conference article in proceeding

SP - 131

EP - 132

BT - Belgian/Netherlands Artificial Intelligence Conference

T2 - 30th Benelux Conference on Artificial Intelligence

Y2 - 8 November 2018 through 9 November 2018

ER -

Indefinite kernel spectral learning

Abstract

Conference

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