Robust Exclusive Adaptive Sparse Feature Selection for Biomarker Discovery and Early Diagnosis of Neuropsychiatric Systemic Lupus Erythematosus

Abstract

The symptoms of neuropsychiatric systemic lupus erythematosus (NPSLE) are subtle and elusive at the early stages. $^1$H-MRS (proton magnetic resonance spectrum) imaging technology can detect more detailed early appearances of NPSLE compared with conventional ones. However, the noises in $^1$H-MRS data often bring bias in the diagnostic process. Moreover, the features of specific brain regions are positively correlated with a certain category but may be redundant for other categories. To overcome these issues, we propose a robust exclusive adaptive sparse feature selection (REASFS) algorithm for early diagnosis and biomarker discovery of NPSLE. Specifically, we employ generalized correntropic loss to address non-Gaussian noise and outliers. Then, we develop a generalized correntropy-induced exclusive $\ell _{2,1}$ regularization to adaptively accommodate various sparsity levels and preserve informative features. We conduct sufficient experiments on a benchmark NPSLE dataset, and the experimental results demonstrate the superiority of our proposed method compared with state-of-the-art ones.