  Diagnosis and therapeutic effect assessment of Parkinson disease based on
voice data are very important,but its few-shot learning problem is
challenging.Although deep learning is good at automatic feature extraction, it
suffers from few-shot learning problem. Therefore, the general effective method
is first conduct feature extraction based on prior knowledge, and then carry
out feature reduction for subsequent classification. However, there are two
major problems: 1) Structural information among speech features has not been
mined and new features of higher quality have not been reconstructed. 2)
Structural information between data samples has not been mined and new samples
with higher quality have not been reconstructed. To solve these two problems,
based on the existing Parkinson speech feature data set, a deep double-side
learning ensemble model is designed in this paper that can reconstruct speech
features and samples deeply and simultaneously. As to feature reconstruction,
an embedded deep stacked group sparse auto-encoder is designed in this paper to
conduct nonlinear feature transformation, so as to acquire new high-level deep
features, and then the deep features are fused with original speech features by
L1 regularization feature selection method. As to speech sample reconstruction,
a deep sample learning algorithm is designed in this paper based on iterative
mean clustering to conduct samples transformation, so as to obtain new
high-level deep samples. Finally, the bagging ensemble learning mode is adopted
to fuse the deep feature learning algorithm and the deep samples learning
algorithm together, thereby constructing a deep double-side learning ensemble
model. At the end of this paper, two representative speech datasets of
Parkinson's disease were used for verification. The experimental results show
that the proposed algorithm are effective.
