Abstract
Early stages of neurodegenerative diseases draw increasing recognition as obscure symptoms may appear before classical clinical diagnosis. For this reason, we propose a novel multi-task low-rank feature learning method, which takes advantages of the sparsity and low-rankness of neuroimaging data for Parkinson’s Disease (PD) and Alzheimer’s Disease (AD) multi-classification. First, the low-rank learning is proposed to unveil the underlying relationships between input data and output targets by preserving the most class-discriminative features. Multi-task learning is simultaneously performed to capture intrinsic feature relatedness. A sparse linear regression framework is designed to find the low-dimensional structure of high dimensional data. Experimental results on the Parkinson’s progression markers initiative (PPMI) and Alzheimer’s disease neuroimaging initiative (ADNI) datasets show that our proposed model not only enhances the performances of multi-classification tasks, but also outperforms the conventional algorithms.
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Acknowledgments
This work was supported partly by National Natural Science Foundation of China (Nos. 61871274, 61801305 and 81571758), National Natural Science Foundation of Guangdong Province (No. 2017A030313377), Guangdong Pearl River Talents Plan (2016ZT06S220), Shenzhen Peacock Plan (Nos. KQTD2016053112051497 and KQTD2015033016104926), and Shenzhen Key Basic Research Project (Nos. JCYJ20180507184647636, JCYJ20170818142347251 and JCYJ20170818094109846).
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Lei, H., Zhao, Y., Lei, B. (2019). Predicting Early Stages of Neurodegenerative Diseases via Multi-task Low-Rank Feature Learning. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11767. Springer, Cham. https://doi.org/10.1007/978-3-030-32251-9_15
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DOI: https://doi.org/10.1007/978-3-030-32251-9_15
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