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Authors: Andrea Gasparetto ; Giorgia Minello and Andrea Torsello

Affiliation: Università Ca' Foscari Venezia, Italy

Keyword(s): Classification, Statistical Learning Framework, Structural Representation, Graph Model.

Related Ontology Subjects/Areas/Topics: Graphical and Graph-Based Models ; Pattern Recognition ; Spectral Methods ; Theory and Methods

Abstract: Graph-based representations have been used with considerable success in computer vision in the abstraction and recognition of object shape and scene structure. Despite this, the methodology available for learning structural representations from sets of training examples is relatively limited. In this paper we take a simple yet effective spectral approach to graph learning. In particular, we define a novel model of structural representation based on the spectral decomposition of graph Laplacian of a set of graphs, but which make away with the need of one-to-one node-correspondences at the base of several previous approaches, and handles directly a set of other invariants of the representation which are often neglected. An experimental evaluation shows that the approach significantly improves over the state of the art.

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Paper citation in several formats:
Gasparetto, A.; Minello, G. and Torsello, A. (2015). A Non-parametric Spectral Model for Graph Classification. In Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM; ISBN 978-989-758-076-5; ISSN 2184-4313, SciTePress, pages 312-319. DOI: 10.5220/0005220303120319

@conference{icpram15,
author={Andrea Gasparetto. and Giorgia Minello. and Andrea Torsello.},
title={A Non-parametric Spectral Model for Graph Classification},
booktitle={Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
year={2015},
pages={312-319},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005220303120319},
isbn={978-989-758-076-5},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
TI - A Non-parametric Spectral Model for Graph Classification
SN - 978-989-758-076-5
IS - 2184-4313
AU - Gasparetto, A.
AU - Minello, G.
AU - Torsello, A.
PY - 2015
SP - 312
EP - 319
DO - 10.5220/0005220303120319
PB - SciTePress

- Science and Technology Publications, Lda.
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