Querying representative points from a pool based on synthesized queries

Hu, Xuelei, Wang, Liantao and Yuan, Bo (2012). Querying representative points from a pool based on synthesized queries. In: The 2012 International Joint Conference on Neural Networks (IJCNN). 2012 International Joint Conference on Neural Networks (IJCNN), Brisbane, Australia, (). 10-15 June 2012. doi:10.1109/IJCNN.2012.6252607

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Author Hu, Xuelei
Wang, Liantao
Yuan, Bo
Title of paper Querying representative points from a pool based on synthesized queries
Conference name 2012 International Joint Conference on Neural Networks (IJCNN)
Conference location Brisbane, Australia
Conference dates 10-15 June 2012
Proceedings title The 2012 International Joint Conference on Neural Networks (IJCNN)   Check publisher's open access policy
Journal name IEEE International Joint Conference on Neural Networks (IJCNN)   Check publisher's open access policy
Place of Publication Piscataway, NJ, United States
Publisher IEEE
Publication Year 2012
Sub-type Fully published paper
DOI 10.1109/IJCNN.2012.6252607
ISBN 9781467314886
9781467314893
ISSN 2161-4393
Total pages 6
Collection year 2013
Language eng
Abstract/Summary How to build a compact and informative training data set autonomously is crucial for many real-world learning tasks, especially those with large amount of unlabeled data and high cost of labeling. Active learning aims to address this problem by asking queries in a smart way. Two main scenarios of querying considered in the literature are query synthesis and pool-based sampling. Since in many cases synthesized queries are meaningless or difficult for human to label, more efforts have been devoted to pool-based sampling in recent years. However, in pool-based active learning, querying requires evaluating every unlabeled data point in the pool, which is usually very time-consuming. By contrast, query synthesis has clear advantage on querying time, which is independent of the pool size. In this paper, we propose a novel framework combining query synthesis and pool-based sampling to accelerate the learning process and overcome the current limitation of query synthesis. The basic idea is to select the data point nearest to the synthesized query as the query point. We also provide two simple strategies for synthesizing informative queries. Moreover, to further speed up querying, we employ clustering techniques on the whole data set to construct a representative unlabeled data pool based on cluster centers. Experiments on several real-world data sets show that our methods have distinct advantages in time complexity and similar performance compared to pool-based uncertainty sampling methods.
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status Non-UQ
Additional Notes The 2012 IEEE World Congress on Computational Intelligence (IEEE-WCCI 2012) consisted of these conferences: the International Joint Conference on Neural Networks (IJCNN 2012), the IEEE International Conference on Fuzzy Systems (FUZZIEEE 2012) and the 2012 IEEE Congress on Evolutionary Computation (IEEE CEC 2012).

 
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Created: Mon, 16 Dec 2013, 22:22:30 EST by Xuelei Hu on behalf of School of Information Technol and Elec Engineering