A Batch-Mode Active Learning Algorithm Using Region-Partitioning Diversity for SVM Classifier

As an emerging technology, brain-computer interfaces (BCIs) bring us new communication interfaces which translate brain activities into control signals for devices like computers, robots, and so forth. In this study, we propose a semisupervised support vector machine (SVM) algorithm for brain-computer interface (BCI) systems, aiming at reducing the time-consuming training process. In this algorithm, we apply a semisupervised SVM for translating the features extracted from the electrical recordings of brain into control signals. This SVM classifier is built from a small labeled data set and a large unlabeled data set. Meanwhile, to reduce the time for training semisupervised SVM, we propose a batch-mode incremental learning method, which can also be easily applied to the online BCI systems. Additionally, it is suggested in many studies that common spatial pattern (CSP) is very effective in discriminating two different brain states. However, CSP needs a sufficient labeled data set. In order to overcome the drawback of CSP, we suggest a two-stage feature extraction method for the semisupervised learning algorithm. We apply our algorithm to two BCI experimental data sets. The offline data analysis results demonstrate the effectiveness of our algorithm.

Download Full-text

Greedy is not Enough: An Efficient Batch Mode Active Learning Algorithm

2009 IEEE International Conference on Data Mining Workshops ◽

10.1109/icdmw.2009.38 ◽

2009 ◽

Cited By ~ 5

Author(s):

Zuobing Xu ◽

Christopher Hogan ◽

Robert Bauer

Keyword(s):

Active Learning ◽

Learning Algorithm ◽

Batch Mode

Download Full-text

An Active Learning Method Based on Variational Autoencoder and DBSCAN Clustering

Computational Intelligence and Neuroscience ◽

10.1155/2021/9952596 ◽

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.

Download Full-text

Active Learning for Relevance Feedback in Image Retrieval

Semantic Mining Technologies for Multimedia Databases ◽

10.4018/978-1-60566-188-9.ch006 ◽

2011 ◽

pp. 152-165

Author(s):

Jian Cheng ◽

Kongqiao Wang ◽

Hanqing Lu

Keyword(s):

Active Learning ◽

Image Retrieval ◽

Relevance Feedback ◽

Learning Algorithm ◽

Texture Feature ◽

Svm Classifier ◽

Learning Approaches ◽

Selective Sampling ◽

Svm Algorithm ◽

Crucial Problem

Relevance feedback is an effective approach to boost the performance of image retrieval. Labeling data is indispensable for relevance feedback, but it is also very tedious and time-consuming. How to alleviate users’ burden of labeling has been a crucial problem in relevance feedback. In recent years, active learning approaches have attracted more and more attention, such as query learning, selective sampling, multi-view learning, and so forth. The well-known examples include Co-training, Co-testing, SVMactive, etc. In this literature, the authors will introduce some representative active learning methods in relevance feedback. Especially, they will present a new active learning algorithm based on multi-view learning, named Co-SVM. In Co-SVM algorithm, color and texture are naturally considered as sufficient and uncorrelated views of an image. SVM classifier is learned in color and texture feature subspaces, respectively. Then the two classifiers are used to classify the unlabeled data. These unlabeled samples that disagree in the two classifiers are chose to label. The extensive experiments show that the proposed algorithm is beneficial to image retrieval.

Download Full-text