Classification Algorithm of Parkinson’s Disease Based on Convolutional Sparse Transfer Learning and Sample/Feature Parallel Selection

Abstract

To solve the problems that there are few labeled data in speech data for diagnosis of Parkinson’s Disease (PD), and the distributed condition of the training and the test data is different, the two aspects of dimension reduction and sample augment are considered. A novel transfer learning algorithm is proposed based on noise weighting sparse coding combined with speech sample / feature parallel selection. The algorithm can learn the structural information from the source domain and express the effective PD features, and achieves dimension reduction and sample augment simultaneously. Considering the relationship between the samples and features, the higher quality features can be extracted. Firstly, the features are extracted from the public data set and the feature data set is constructed as source domain. Then the training data and test data of the target domain are sparsely represented based on source domain. Spares representing includs traditional Sparse Coding(SC) and Convolutional Sparse Coding(CSC); Next, the sparse representing data are screened according to sample feature selection simultaneously, so as to improve the accuracy of the PD classification; Finally, the Support Vector Machine(SVM) classifier is adopted. Experiments show that it achieves the highest classification accuracy of 95.0% and the average classification accuracy of 86.0%, and obtains obvious improvement according to the subjects, compared with the relevant algorithms. Besides, compared with sparse coding, convolutional sparse coding can be beneficial to extracting high level features from PD data set; moreover, it is proved that transfer learning is effective.