  Labeled speech data from patients with Parkinsons disease (PD) are scarce,
and the statistical distributions of training and test data differ
significantly in the existing datasets. To solve these problems, dimensional
reduction and sample augmentation must be considered. In this paper, a novel PD
classification algorithm based on sparse kernel transfer learning combined with
a parallel optimization of samples and features is proposed. Sparse transfer
learning is used to extract effective structural information of PD speech
features from public datasets as source domain data, and the fast ADDM
iteration is improved to enhance the information extraction performance. To
implement the parallel optimization, the potential relationships between
samples and features are considered to obtain high-quality combined features.
First, features are extracted from a specific public speech dataset to
construct a feature dataset as the source domain. Then, the PD target domain,
including the training and test datasets, is encoded by convolution sparse
coding, which can extract more in-depth information. Next, parallel
optimization is implemented. To further improve the classification performance,
a convolution kernel optimization mechanism is designed. Using two
representative public datasets and one self-constructed dataset, the
experiments compare over thirty relevant algorithms. The results show that when
taking the Sakar dataset, MaxLittle dataset and DNSH dataset as target domains,
the proposed algorithm achieves obvious improvements in classification
accuracy. The study also found large improvements in the algorithms in this
paper compared with nontransfer learning approaches, demonstrating that
transfer learning is both more effective and has a more acceptable time cost.
