Sparse Manifold Learning with Applications to SAR Image Classification

Nonlinear data-driven dimensionality reduction techniques have recently gained popularity due to the emergence of high dimensional data sets. The algorithmic complexity and storage requirements of these techniques, however, can make them prohibitive in resource-limited applications. It is therefore...

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Bibliographic Details
Main Authors: Berisha, V., Shah, N., Waagen, D., Schmitt, H., Bellofiore, S., Spanias, A., Cochran, D.
Format: Conference Proceeding
Language:English
Subjects:
Azimuth
classification
Computational complexity
Data mining
dimensionality reduction
Image classification
out-of-sample extension
Performance evaluation
Pixel
reduced complexity Isomap
SAR
Synthetic aperture radar
Target recognition
Testing
Vehicles
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Description
Summary:Nonlinear data-driven dimensionality reduction techniques have recently gained popularity due to the emergence of high dimensional data sets. The algorithmic complexity and storage requirements of these techniques, however, can make them prohibitive in resource-limited applications. It is therefore beneficial to reduce the number of exemplar samples required for performing an out-of-sample extension to a test point. In this paper, we propose a novel method for selecting a minimal set of exemplars and performing the out-of-sample extension. In the case of two-class target recognition with synthetic aperture radar (SAR) data, we compare the efficacy of the proposed approach with other approaches for selecting a subset of the available training samples. We show that the proposed algorithm outperforms the existing methods by providing low-dimensional embeddings that maintain interclass separability using fewer retained exemplars.
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2007.366873