Learning dimensionality-reduced classifiers for information fusion
Name
Varshney-2009-Learning dimensionality-reduced classifiers for information fusion.pdf
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1.88 MB
Format
Adobe PDF
Checksum (MD5)
a1d013b745bd1f7ff1a73b7927605a06
Author(s)
Varshney, Kush R.
Willsky, Alan S.
Date Issued
August 2009
Journal
Proceedings of the 12th International Conference on Information Fusion, 2009. FUSION '09
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Kush R. Varshney and Alan S. Willsky. "Learning dimensionality-reduced classifiers for information fusion." 12th International Conference on Information Fusion, 2009. FUSION '09. © 2009 ISIF
Version
Final published version
Abstract
The fusion of multimodal sensor information often requires learning decision rules from samples of high-dimensional data. Each data dimension may only be weakly informative for the detection problem of interest. Also, it is not known a priori which components combine to form a lower-dimensional feature space that is most informative. To learn both the combination of dimensions and the decision rule specified in the reduced-dimensional space together, we jointly optimize the linear dimensionality reduction and margin-based supervised classification problems, representing dimensionality reduction by matrices on the Stiefel manifold. We describe how the learning procedure and resulting decision rule can be implemented in parallel, serial, and tree-structured fusion networks.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
http://ieeexplore.ieee.org/xpls/abs_all.jsp?tp=&arnumber=5203616&tag=1
