Brain Status Prediction with Non-negative Projective Dictionary Learning
Study on brain status prediction has recently received increasing attention from the research community. In this paper, we propose to tackle brain status prediction by learning a discriminative representation of the data with a novel non-negative projective dictionary learning (NPDL) approach. The proposed approach performs class-wise projective dictionary learning, which uses an analysis dictionary to generate non-negative coding vectors from the data, and a synthesis dictionary to reconstruct the data. We formulate the learning problem as a constrained non-convex optimization problem and solve it via an alternating direction method of multipliers (ADMM). To investigate the effectiveness of the proposed approach on brain status prediction, we conduct experiments on two datasets, ADNI and NIH Study of Normal Brain Development repository, and report superior results over comparison methods.
This work is supported by HBHL FRQ/CCC Axis X-C (Funding No. 246117), Canada, NSFC Joint Fund with Zhejiang Integration of Informatization and Industrialization under Key Project (U1609218), China.
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