Longitudinal and Multi-modal Data Learning via Joint Embedding and Sparse Regression for Parkinson's Disease Diagnosis

Parkinson's disease (PD) is a neurodegenerative progressive disease that mainly affects the motor systems of patients. To slow this disease deterioration, robust and accurate diagnosis of PD is an effective way to alleviate mental and physical sufferings of clinical intervention. In this paper, we propose a new unsupervised feature selection method via joint embedding learning and sparse regression using longitudinal multi-modal neuroimaging data. Specifically, the proposed method performs feature selection and local structure learning, simultaneously, to adaptively determine the similarity matrix. Meanwhile, we constrain the similarity matrix to make it contains c connected components for gaining the most accurate information of the neuroimaging data structure. The baseline data is utilized to establish the feature selection model to select the most discriminative features. Namely, we exploit baseline data to train four regression models for the clinical scores prediction (depression, sleep, olfaction, and cognition scores) and a classification model for the classification of PD disease in the future time point. Extensive experiments are conducted to demonstrate the effectiveness of the proposed method on the Parkinson's Progression Markers Initiative (PPMI) dataset. The experimental results demonstrate that, our proposed method can enhance the performance in clinical scores prediction and class label identification in longitudinal data and outperforms the state-of-art methods as well.