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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/12351

Title: An effective framework for supervised dimension reduction
Authors: Than, Khoat
Ho, Tu Bao
Nguyen, Duy Khuong
Keywords: supervised dimension reduction
sparse modeling
topic model
Issue Date: 2014-04-08
Publisher: Elsevier
Magazine name: Neurocomputing
Volume: 139
Start page: 397
End page: 407
DOI: 10.1016/j.neucom.2014.02.017
Abstract: We consider supervised dimension reduction (SDR) for problems with discrete inputs. Existing methods are computationally expensive, and often do not take the local structure of data into consideration when searching for a low-dimensional space. In this paper, we propose a novel framework for SDR with the aims that it can inherit scalability of existing unsupervised methods, and that it can exploit well label information and local structure of data when searching for a new space. The way we encode local information in this framework ensures three effects: preserving inner-class local structure, widening inter-class margin, and reducing possible overlap between classes. These effects are vital for success in practice. Such an encoding helps our framework succeed even in cases that data points reside in a nonlinear manifold, for which existing methods fail. The framework is general and flexible so that it can be easily adapted to various unsupervised topic models. We then adapt our framework to three unsupervised models which results in three methods for SDR. Extensive experiments on 10 practical domains demonstrate that our framework can yield scalable and qualitative methods for SDR. In particular, one of the adapted methods can perform consistently better than the state- of-the-art method for SDR while enjoying 30-450 times faster speed.
Rights: NOTICE: This is the author’s version of a work accepted for publication by Elsevier. Changes resulting from the publishing process, including peer review, editing, corrections, structural formatting and other quality control mechanisms, may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Khoat Than, Tu Bao Ho, Duy Khuong Nguyen, Neurocomputing, 139, 2014, 397-407, http://dx.doi.org/10.1016/j.neucom.2014.02.017
URI: http://hdl.handle.net/10119/12351
Material Type: author
Appears in Collections:a10-1. 雑誌掲載論文 (Journal Articles)

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