Conference Proceeding Article
Relative similarity learning, as an important learning scheme for information retrieval, aims to learn a bi-linear similarity function from a collection of labeled instance-pairs, and the learned function would assign a high similarity value for a similar instance-pair and a low value for a dissimilar pair. Existing algorithms usually assume the labels of all the pairs in data streams are always made available for learning. However, this is not always realistic in practice since the number of possible pairs is quadratic to the number of instances in the database, and manually labeling the pairs could be very costly and time consuming. To overcome the limitation, we propose a novel framework of active online similarity learning. Specifically, we propose two new algorithms: (i)~PAAS: Passive-Aggressive Active Similarity learning; (ii)~CWAS: Confidence-Weighted Active Similarity learning, and we will prove their mistake bounds in theory. We have conducted extensive experiments on a variety of real-world data sets, and we find encouraging results that validate the empirical effectiveness of the proposed algorithms.
machine learning, data streams, online learning
Databases and Information Systems
Data Management and Analytics
CIKM 2015: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management: Melbourne, Australia, October 19-23, 2015
City or Country
Shuji Hao; Peilin Zhao; HOI, Steven C. H.; and Chunyan Miao.
Learning relative similarity from data streams: Active online learning approaches. (2015). CIKM 2015: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management: Melbourne, Australia, October 19-23, 2015. 1181-1190. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2925
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