Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/107911
Type: Conference paper
Title: Learning sparse confidence-weighted classifier on very high dimensional data
Author: Tan, M.
Yan, Y.
Wang, L.
Van Den Hengel, A.
Tsang, I.
Shi, Q.
Citation: Proceedings of the 30th AAAI Conference on Artificial Intelligence, 2016 / vol.3, pp.2080-2086
Publisher: AAAI Press
Issue Date: 2016
ISBN: 9781577357605
ISSN: 2159-5399
2374-3468
Conference Name: 30th AAAI Conference on Artificial Intelligence (AAAI) (12 Feb 2016 - 17 Feb 2016 : Phoenix, AZ)
Statement of
Responsibility: 
Mingkui Tan, Yan Yan, Li Wang, Anton Van Den Hengel, IvorW. Tsang, Qinfeng, Javen, Shi
Abstract: Confidence-weighted (CW) learning is a successful online learning paradigm which maintains a Gaussian distribution over classifier weights and adopts a covariance matrix to represent the uncertainties of the weight vectors. However, there are two deficiencies in existing full CW learning paradigms, these being the sensitivity to irrelevant features, and the poor scalability to high dimensional data due to the maintenance of the covariance structure. In this paper, we begin by presenting an online-batch CW learning scheme, and then present a novel paradigm to learn sparse CW classifiers. The proposed paradigm essentially identifies feature groups and naturally builds a block diagonal covariance structure, making it very suitable for CW learning over very high-dimensional data. Extensive experimental results demonstrate the superior performance of the proposed methods over state-of-the-art counterparts on classification and feature selection tasks.
Rights: Copyright © 2016, Association for the Advancement of Artificial Intelligence
RMID: 0030062418
Grant ID: http://purl.org/au-research/grants/arc/DP140102270
http://purl.org/au-research/grants/arc/FT130100746
http://purl.org/au-research/grants/arc/LP150100671
Appears in Collections:Computer Science publications

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