19 May 2005 Random subspaces and SAR classification efficacy
Author Affiliations +
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.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Donald Waagen, Donald Waagen, Nitesh Shah, Nitesh Shah, Miguel Ordaz, Miguel Ordaz, Mary Cassabaum, Mary Cassabaum, } "Random subspaces and SAR classification efficacy", Proc. SPIE 5808, Algorithms for Synthetic Aperture Radar Imagery XII, (19 May 2005); doi: 10.1117/12.602523; https://doi.org/10.1117/12.602523

Back to Top