Multi-task Sparse Low-Rank Learning for Multi-classification of Parkinson’s Disease

Abstract

Identifying prodromal stages of Parkinson’s disease (PD) draws increasing recognition as non-motor symptoms may appear before classical clinical diagnosis based on motor signs. To effectively develop a computer-aided diagnosis for multiple disease progression stages, neuroimaging has been widely applied for its convenience of revealing the intricate brain structure. However, the high dimensional neuroimaging features and limited sample size bring the main challenges for the diagnosis task. To handle it, a multi-task sparse low-rank learning framework is proposed to unveil the underlying relationships between input data and output targets by building a matrix-regularized feature network. Inductions of multiple tasks are simultaneously performed to capture intrinsic feature relatedness with multi-task learning. By discarding the irrelevant features and preserving the discriminative structured features, our proposed method can select the most relevant features and identify different stages of PD with different multi-classification models. Extensive experimental results on the Parkinson’s progression markers initiative (PPMI) dataset demonstrate that the proposed method achieves promising classification performance and outperforms the conventional algorithms.