Adaptive Sparse Learning for Neurodegenerative Disease Classification

This paper proposed an adaptive sparse learning (ASL) framework to solve the multi-classification problem for neurodegenerative disease analysis. Specifically, we integrate the idea of feature selection and subspace learning to construct a least square regression model. The principle of Fisher's linear discriminant analysis (LDA) and locality preserving projection (LPP) are incorporated to utilize the global and local information in the original data space. Additionally, we introduce a generalized norm to the loss function to regulate the sparseness degree. This framework can select the most relative and distinguishable features to enhance classification performance. Unlike most previous methods for binary classification, we perform a multi classification to improve the efficiency of computer-aided diagnosis. Our proposed method is validated on the public available Parkinson's progression markers initiative (PPMI) and Alzheimer's disease neuroimaging initiative (ADNI) datasets. Experimental results show that our proposed method can identify subjects more accurately compared to other state-of-the-art methods.