Batch Mode Active Learning Algorithm Combining with Self-training for Multiclass Brain-computer Interfaces

2015 ◽  
Vol 12 (6) ◽  
pp. 2351-2359 ◽  
Author(s):  
Minyou Chen
Keyword(s):  
Active Learning ◽  
Learning Algorithm ◽  
Brain Computer Interfaces ◽  
Batch Mode ◽  
Computer Interfaces

scholarly journals An Active Learning Method Based on Variational Autoencoder and DBSCAN Clustering

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Fang Chen ◽  
Tao Zhang ◽  
Ruilin Liu
Keyword(s):  
Active Learning ◽  
Learning Algorithm ◽  
Clustering Algorithms ◽  
Batch Size ◽  
Learning Method ◽  
Clustering Methods ◽  
High Dimensions ◽  
Batch Mode ◽  
Variational Autoencoder ◽  
Active Learning Method

Active learning is aimed to sample the most informative data from the unlabeled pool, and diverse clustering methods have been applied to it. However, the distance-based clustering methods usually cannot perform well in high dimensions and even begin to fail. In this paper, we propose a new active learning method combined with variational autoencoder (VAE) and density-based spatial clustering of applications with noise (DBSCAN). It overcomes the difficulty of distance representation in high dimensions and prevents the distance concentration phenomenon from occurring in the computational learning literature with respect to high-dimensional p-norms. Finally, we compare our method with four common active learning methods and two other clustering algorithms combined with VAE on three datasets. The results demonstrate that our approach achieves competitive performance, and it is a new batch mode active learning algorithm designed for neural networks with a relatively small query batch size.

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