Regularized Bagged Canonical Component Analysis for Multiclass Learning in Brain Imaging.
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.
['Sevilla-Salcedo|Carlos|C|0000-0002-5507-7537', 'Gómez-Verdejo|Vanessa|V|0000-0001-7702-8747', 'Tohka|Jussi|J|', '|||']
D000368:Aged D000369:Aged, 80 and over D000544:Alzheimer Disease D001289:Attention Deficit Disorder with Hyperactivity D001921:Brain D002648:Child D060825:Cognitive Dysfunction D018450:Disease Progression D005260:Female D006801:Humans D007090:Image Interpretation, Computer-Assisted D000069550:Machine Learning D008279:Magnetic Resonance Imaging D008297:Male D059906:Neuroimaging D010363:Pattern Recognition, Automated
Brain imaging Feature selection Canonical correlation analysis Multiclass classification