Article

Regularized Bagged Canonical Component Analysis for Multiclass Learning in Brain Imaging

2019-07-11

Abstract excerpt

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 determin...

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Literature Corpus work: 8fb89881-d171-59b5-bac2-fea0d36d3d2e
DOI: 10.1101/698134

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