Learning dimensionality-reduced classifiers for information fusion
Author(s)
Varshney, Kush R.; Willsky, Alan S.
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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.
Date issued
2009-08Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science; Massachusetts Institute of Technology. Laboratory for Information and Decision SystemsJournal
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
ISBN
978-0-9824-4380-4