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http://hdl.handle.net/10397/105677
Title: | Multi-label learning with global density fusion mapping features | Authors: | Guo, Y Chung, F Li, G |
Issue Date: | 2016 | Source: | 2016 23rd International Conference on Pattern Recognition (ICPR), Cancún Center, Cancún, México, December 4-8, 2016, p. 462-467 | Abstract: | Multi-label learning, where each instance is assigned to multiple categories simultaneously, is a prevalent problem in data analysis. Previous study approaches typically learn from multi-label data by employing the original feature space in the discrimination process of all class labels. However, this traditional strategy might be suboptimal as the original feature space exists irrelevant or redundant information, which affect the performance of classification. In this paper, we propose another strategy to learn from multi-label data, where reconstructed feature space is exploited to improve the classification performance. Accordingly, an intuitive yet effective algorithm named ATOM, i.e. multi-label learning with globAl densiTy fusiOn Mapping features, is proposed. ATOM firstly reconstructs feature spaces specific to each and no label by conducting cluster analysis on its belonging instances, and then utilizes density fusion to excavate optimum centers from the cluster center union, and finally performs classification by querying the reconstructed feature spaces. Comprehensive experimental results on a total of 12 benchmark data sets clearly validate the superiority of ATOM against other competitive algorithms. | Publisher: | Institute of Electrical and Electronics Engineers | ISBN: | 978-1-5090-4847-2 (Electronic) 978-1-5090-4848-9 (Print on Demand(PoD)) |
DOI: | 10.1109/ICPR.2016.7899677 | Rights: | ©2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The following publication Yumeng Guo, Fulai Chung and Guozheng Li, "Multi-label learning with globAl densiTy fusiOn Mapping features," 2016 23rd International Conference on Pattern Recognition (ICPR), Cancun, Mexico, 2016, pp. 462-467 is available at https://doi.org/10.1109/ICPR.2016.7899677. |
Appears in Collections: | Conference Paper |
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