Hybrid dendritic computing with kernel-LICA applied to Alzheimer's disease detection in MRI

Dendritic computing has been proved to produce perfect approximation of any data distribution. This result guarantees perfect accuracy training. However, we have found great performance degradation when tested on conventional k-fold cross-validation schemes. In this paper we propose to use Lattice I...

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Bibliographic Details
Published inNeurocomputing (Amsterdam) Vol. 75; no. 1; pp. 72 - 77
Main Authors Chyzhyk, Darya, Graña, Manuel, Savio, Alexandre, Maiora, Josu
Format Journal Article
LanguageEnglish
Published Elsevier B.V 2012
Subjects
Alzheimer's disease
Dendritic computing
Kernel method
Lattice computing
Lattice Independent Component Analysis
Dendritic computing
Lattice computing
Alzheimer's disease
Lattice Independent Component Analysis
Kernel method
Online AccessGet full text
ISSN0925-2312
1872-8286
DOI10.1016/j.neucom.2011.02.024

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Summary:Dendritic computing has been proved to produce perfect approximation of any data distribution. This result guarantees perfect accuracy training. However, we have found great performance degradation when tested on conventional k-fold cross-validation schemes. In this paper we propose to use Lattice Independent Component Analysis (LICA) and the Kernel transformation of the data as an appropriate feature extraction that improves the generalization of dendritic computing classifiers. We obtain a big increase in classification performance applying with this schema over a database of features extracted from Magnetic Resonance Imaging (MRI) including Alzheimer's disease (AD) patients and control subjects.
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ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2011.02.024