Random subspaces and SAR classification efficacy

Donald Waagen; Nitesh Shah; Miguel Ordaz; Mary Cassabaum

doi:10.1117/12.602523

19 May 2005 Random subspaces and SAR classification efficacy

Donald Waagen, Nitesh Shah, Miguel Ordaz, Mary Cassabaum

Proceedings Volume 5808, Algorithms for Synthetic Aperture Radar Imagery XII; (2005) https://doi.org/10.1117/12.602523
Event: Defense and Security, 2005, Orlando, Florida, United States

Abstract

The 'curse of dimensionality' has limited the application of statistical modeling techniques to low-dimensional spaces, but typical data usually resides in high-dimensional spaces (at least initially, for instance images represented as arrays of pixel values). Indeed, approaches such as Principal Component Analysis and Independent Component Analysis attempt to extract a set of meaningful linear projections while minimizing interpoint distance distortions. The counterintuitive yet effective random projections approach of Johnson and Lindenstrauss defines a sample-based dimensionality reduction technique with probabilistically provable distortion bounds. We investigate and report on the relative efficacy of two random projection techniques for Synthetic Aperture Radar images in a classification setting.

Citation Download Citation

Donald Waagen, Nitesh Shah, Miguel Ordaz, and Mary Cassabaum "Random subspaces and SAR classification efficacy", Proc. SPIE 5808, Algorithms for Synthetic Aperture Radar Imagery XII, (19 May 2005); https://doi.org/10.1117/12.602523

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