On the definition of complex structured feature spaces

Nicolò Navarin; Dinh V. Tran; Alessandro Sperduti

On the definition of complex structured feature spaces

Nicolò Navarin, Dinh V. Tran, Alessandro Sperduti

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

2 Scopus citations

Abstract

In this paper, we propose a graph kernel whose feature space is defined by combining pairs of features of an existing base graph kernel. Furthermore, we propose a variation where the feature space is adaptive with respect to the learning task at hand, allowing to learn a representation suited to it. Experimental results on six real-world graph datasets from different domains show that the proposed kernels are able to get a consistent performance improvement over the considered base kernel, and over previously defined feature combination methods in literature.

Original language	English
Title of host publication	ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Publisher	ESANN (i6doc.com)
Pages	101-106
Number of pages	6
ISBN (Electronic)	9782875870650
State	Published - 2019
Externally published	Yes
Event	27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2019 - Bruges, Belgium Duration: 24 Apr 2019 → 26 Apr 2019

Publication series

Name	ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Conference

Conference	27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2019
Country/Territory	Belgium
City	Bruges
Period	24/04/19 → 26/04/19

Bibliographical note

Publisher Copyright:
© 2019 ESANN (i6doc.com). All rights reserved.

ASJC Scopus subject areas

Artificial Intelligence
Information Systems

Cite this

Navarin, N., Tran, D. V., & Sperduti, A. (2019). On the definition of complex structured feature spaces. In ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 101-106). (ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning). ESANN (i6doc.com).

Navarin, Nicolò ; Tran, Dinh V. ; Sperduti, Alessandro. / On the definition of complex structured feature spaces. ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. ESANN (i6doc.com), 2019. pp. 101-106 (ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning).

@inproceedings{035cd26ac2434727a533264406527b86,

title = "On the definition of complex structured feature spaces",

abstract = "In this paper, we propose a graph kernel whose feature space is defined by combining pairs of features of an existing base graph kernel. Furthermore, we propose a variation where the feature space is adaptive with respect to the learning task at hand, allowing to learn a representation suited to it. Experimental results on six real-world graph datasets from different domains show that the proposed kernels are able to get a consistent performance improvement over the considered base kernel, and over previously defined feature combination methods in literature.",

author = "Nicol{\`o} Navarin and Tran, {Dinh V.} and Alessandro Sperduti",

note = "Publisher Copyright: {\textcopyright} 2019 ESANN (i6doc.com). All rights reserved.; 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2019 ; Conference date: 24-04-2019 Through 26-04-2019",

year = "2019",

language = "English",

series = "ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning",

publisher = "ESANN (i6doc.com)",

pages = "101--106",

booktitle = "ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning",

}

Navarin, N, Tran, DV & Sperduti, A 2019, On the definition of complex structured feature spaces. in ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN (i6doc.com), pp. 101-106, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2019, Bruges, Belgium, 24/04/19.

On the definition of complex structured feature spaces. / Navarin, Nicolò; Tran, Dinh V.; Sperduti, Alessandro.
ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. ESANN (i6doc.com), 2019. p. 101-106 (ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning).

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

TY - GEN

T1 - On the definition of complex structured feature spaces

AU - Navarin, Nicolò

AU - Tran, Dinh V.

AU - Sperduti, Alessandro

PY - 2019

Y1 - 2019

N2 - In this paper, we propose a graph kernel whose feature space is defined by combining pairs of features of an existing base graph kernel. Furthermore, we propose a variation where the feature space is adaptive with respect to the learning task at hand, allowing to learn a representation suited to it. Experimental results on six real-world graph datasets from different domains show that the proposed kernels are able to get a consistent performance improvement over the considered base kernel, and over previously defined feature combination methods in literature.

AB - In this paper, we propose a graph kernel whose feature space is defined by combining pairs of features of an existing base graph kernel. Furthermore, we propose a variation where the feature space is adaptive with respect to the learning task at hand, allowing to learn a representation suited to it. Experimental results on six real-world graph datasets from different domains show that the proposed kernels are able to get a consistent performance improvement over the considered base kernel, and over previously defined feature combination methods in literature.

UR - http://www.scopus.com/inward/record.url?scp=85071307876&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:85071307876

T3 - ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

SP - 101

EP - 106

BT - ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

PB - ESANN (i6doc.com)

T2 - 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2019

Y2 - 24 April 2019 through 26 April 2019

ER -

Navarin N, Tran DV, Sperduti A. On the definition of complex structured feature spaces. In ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. ESANN (i6doc.com). 2019. p. 101-106. (ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning).

On the definition of complex structured feature spaces

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus subject areas

Fingerprint

Cite this