Indefinite kernel spectral learning

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

Indefinite kernel spectral learning

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

Sub Algorithmic Data Analysis

Shanghai Jiao Tong University
KU Leuven

Research output: Contribution to journal › Conference article › 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
Pages (from-to)	131-132
Number of pages	2
Journal	Belgian/Netherlands Artificial Intelligence Conference
Publication status	Published - 2018
Event	30th Benelux Conference on Artificial Intelligence, BNAIC 2018 - s-Hertogenbosch, Netherlands Duration: 8 Nov 2018 → 9 Nov 2018

Bibliographical 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:
© 2018 University of Groningen. All rights reserved.

Funding

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. 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.

Cite this

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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 ; Conference date: 08-11-2018 Through 09-11-2018",

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AU - Huang, Xiaolin

AU - Suykens, Johan A.K.

N1 - 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: © 2018 University of Groningen. All rights reserved.

PY - 2018

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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.

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AN - SCOPUS:85072679782

SN - 1568-7805

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JO - Belgian/Netherlands Artificial Intelligence Conference

JF - Belgian/Netherlands Artificial Intelligence Conference

T2 - 30th Benelux Conference on Artificial Intelligence, BNAIC 2018

Y2 - 8 November 2018 through 9 November 2018

ER -

Indefinite kernel spectral learning

Abstract

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