http://www.cnr.it/ontology/cnr/individuo/prodotto/ID214722
Information-Theoretic Selection of High-Dimensional Spectral Features for Structural Recognition (Articolo in rivista)
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- Label
- Information-Theoretic Selection of High-Dimensional Spectral Features for Structural Recognition (Articolo in rivista) (literal)
- Anno
- 2013-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.1016/j.cviu.2012.11.007 (literal)
- Alternative label
Boyan B., Escolano F., Giorgi D., Biasotti S. (2013)
Information-Theoretic Selection of High-Dimensional Spectral Features for Structural Recognition
in Computer vision and image understanding (Print); ELSEVIER, NEW YORK (Stati Uniti d'America)
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- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- Boyan B., Escolano F., Giorgi D., Biasotti S. (literal)
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- http://www.sciencedirect.com/science/article/pii/S1077314212001919 (literal)
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- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
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- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- University of Alicante, Spain; University of Alicante, Spain; CNR-ISTI, Pisa, Italy; CNR-IMATI, Genova, Italy (literal)
- Titolo
- Information-Theoretic Selection of High-Dimensional Spectral Features for Structural Recognition (literal)
- Abstract
- Pattern recognition methods often deal with samples consisting of thousands of features. Therefore, the reduction of their dimensionality becomes crucial to make the data sets tractable. Feature selection techniques
remove the irrelevant and noisy features and select a subset of features which describe better the samples and produce a better classification performance. In this paper, we propose a novel feature selection
method for supervised classification within an information-theoretic framework. Mutual information is exploited for measuring the statistical relation between a subset of features and the class labels
of the samples. Traditionally it has been measured for ranking single features; however, in most data sets the features are not independent and their combination provides much more information about the class
than the sum of their individual prediction power. We analyze the use of different estimation methods which bypass the density estimation and estimate entropy and mutual information directly from the
set of samples. These methods allow us to efficiently evaluate multivariate sets of thousands of features.
Within this framework we experiment with spectral graph features extracted from 3D shapes. Most of the existing graph classification techniques rely on the graph attributes. We use unattributed graphs to show what is the contribution of each spectral feature to graph classification. Apart from succeeding to classify graphs from shapes relying only on their structure, we test to what extent the set of selected spectral features are robust to perturbations of the dataset. (literal)
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