2014 ActiveLearningforSparseBayesian

• (Vasisht et al., 2014) ⇒ Deepak Vasisht, Andreas Damianou, Manik Varma, and Ashish Kapoor. (2014). “Active Learning for Sparse Bayesian Multilabel Classification.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623759

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222014%22+Active+Learning+for+Sparse+Bayesian+Multilabel+Classification
• http://dl.acm.org/citation.cfm?id=2623330.2623759&preflayout=flat#citedby

Quotes

Author Keywords

• Active learning; learning; multi-label learning; mutual information

Abstract

We study the problem of active learning for multilabel classification. We focus on the real-world scenario where the average number of positive (relevant) labels per data point is small leading to positive label sparsity. Carrying out mutual information based near-optimal active learning in this setting is a challenging task since the computational complexity involved is exponential in the total number of labels. We propose a novel inference algorithm for the sparse Bayesian multilabel model of [17]. The benefit of this alternate inference scheme is that it enables a natural approximation of the mutual information objective. We prove that the approximation leads to an identical solution to the exact optimization problem but at a fraction of the optimization cost. This allows us to carry out efficient, non-myopic, and near-optimal active learning for sparse multilabel classification. Extensive experiments reveal the effectiveness of the method.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 ActiveLearningforSparseBayesian	Manik Varma Deepak Vasisht Andreas Damianou Ashish Kapoor			Active Learning for Sparse Bayesian Multilabel Classification				10.1145/2623330.2623759		2014