Matrix Linear Discriminant Analysis.
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TL;DR: A novel linear discriminant analysis approach for the classification of high-dimensional matrix-valued data that commonly arises from imaging studies is proposed using an efficient nuclear norm penalized regression that encourages a low-rank structure.
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Abstract: We propose a novel linear discriminant analysis (LDA) approach for the classification of high-dimensional matrix-valued data that commonly arises from imaging studies. Motivated by the equivalence ...
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Dual-source dual-energy computed tomography-derived quantitative parameters combined with machine learning for the differential diagnosis of benign and malignant thyroid nodules
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Nonparametric matrix response regression with application to brain imaging data analysis.
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Dual-source dual-energy computed tomography-derived quantitative parameters combined with machine learning for the differential diagnosis of benign and malignant thyroid nodules
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TL;DR: In this article , the ability of quantitative parameter-derived dual-source dual-energy computed tomography (DS-DECT) combined with machine learning to distinguish between benign and malignant thyroid nodules was investigated.
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Logistic regression with image covariates via the combination of L1 and Sobolev regularizations.
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