Deep Double-Side Learning Ensemble Model for Few-Shot Parkinson Speech Recognition

no code implementations • 20 Jun 2020 • Yongming Li, Lang Zhou, Lingyun Qin, Yuwei Zeng, Yuchuan Liu, Yan Lei, Pin Wang, Fan Li

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.

Clustering Ensemble Learning +4

Hybrid Embedded Deep Stacked Sparse Autoencoder with w_LPPD SVM Ensemble

no code implementations • 17 Feb 2020 • Yongming Li, Yan Lei, Pin Wang, Yuchuan Liu

For the issue that class representation ability of abstract information is limited by small sample problem, a feature fusion strategy has been designed aiming to combining abstract information learned by HFESAE with original feature and obtain hybrid features for feature reduction.

feature selection

Classification Algorithm of Speech Data of Parkinsons Disease Based on Convolution Sparse Kernel Transfer Learning with Optimal Kernel and Parallel Sample Feature Selection

no code implementations • 10 Feb 2020 • Xiaoheng Zhang, Yongming Li, Pin Wang, Xiaoheng Tan, Yuchuan Liu

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.

feature selection General Classification +1