no code implementations • 25 Oct 2022 • Chuanyan Zhou, Jie Ma, Fan Li, Yongming Li, Pin Wang, Xiaoheng Zhang
Second, an embedded stack autoencoder (ESAE) is proposed and trained in each layer of sample space to consider the original samples during training and in the network structure, thereby better finding the relationship between original feature samples and deep feature samples.
no code implementations • 25 Jun 2022 • Fan Li, Xiaoheng Zhang, Yongming Li, Pin Wang
Based on the analysis above, an imbalanced ensemble algorithm with the deep sample pre-envelope network (DSEN) and local-global structure consistency mechanism (LGSCM) is proposed here to solve the problem. This algorithm can guarantee high-quality deep envelope samples for considering the local manifold and global structures information, which is helpful for imbalance learning.
no code implementations • 17 Nov 2021 • Yiwen Wang, Fan Li, Xiaoheng Zhang, Pin Wang, Yongming Li
Therefore, it is necessary to reconstruct the existing large segments within one subject into few segments even one segment within one subject, which can facilitate the extraction of relevant speech features to characterize diagnostic markers for the whole subject.
no code implementations • 2 Nov 2021 • Fan Li, Xiaoheng Zhang, Pin Wang, Yongming Li
However, all existing clustering methods are based on a one-time approach.
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.