Regularized Bagged Canonical Component Analysis for Multiclass Learning in Brain Imaging
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ORIGINAL ARTICLE
Regularized Bagged Canonical Component Analysis for Multiclass Learning in Brain Imaging 1 ´ Carlos Sevilla-Salcedo1 · Vanessa Gomez-Verdejo Alzheimer’s Disease Neuroimaging Initiative
· Jussi Tohka2
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© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract A fundamental problem of supervised learning algorithms for brain imaging applications is that the number of features far exceeds the number of subjects. In this paper, we propose a combined feature selection and extraction approach for multiclass problems. This method starts with a bagging procedure which calculates the sign consistency of the multivariate analysis (MVA) projection matrix feature-wise to determine the relevance of each feature. This relevance measure provides a parsimonious matrix, which is combined with a hypothesis test to automatically determine the number of selected features. Then, a novel MVA regularized with the sign and magnitude consistency of the features is used to generate a reduced set of summary components providing a compact data description. We evaluated the proposed method with two multiclass brain imaging problems: 1) the classification of the elderly subjects in four classes (cognitively normal, stable mild cognitive impairment (MCI), MCI converting to AD in 3 years, and Alzheimer’s disease) based on structural brain imaging data from the ADNI cohort; 2) the classification of children in 3 classes (typically developing, and 2 types of Attention Deficit/Hyperactivity Disorder (ADHD)) based on functional connectivity. Experimental results confirmed that each brain image (defined by 29.852 features in the ADNI database and 61.425 in the ADHD) could be represented with only 30− 45% of the original features. Furthermore, this information could be redefined into two or three summary components, providing not only a gain of interpretability but also classification rate improvements when compared to state-of-art reference methods. Keywords Canonical correlation analysis · Multiclass classification · Feature selection · Brain imaging
Introduction Machine Learning (ML) techniques can be used for the design of imaging biomarkers for various brain disorders Alzheimer’s Disease Neuroimaging Initiative (ADNI) is a Group/Institutional Author. Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/ uploads/how to apply/ADNI Acknowledgement List.pdf Electronic supplementary material The online version of this article (https://doi.org/10.1007/s12021-020-09470-y) contains supplementary material, which is available to authorized users. Carlos Sevilla-Salcedo
[email protected]
Extended author information available on the last page of the a
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