Dimensionality Reduction for Integrated Sensing and Processing

Thursday, December 8, 2005 - 9:00am - 10:00am
EE/CS 3-180
Alfred Hero III (University of Michigan)
Dimensionality reduction methods have played a central role in exploration of parsimonious structural models, complexity regularization
in inverse problems, and data compression. Examples are PCA, Laplacian eigenmaps, ISOMAP, and matching pursuits which attempt to fit a
subspace to the data. For integrated sensing and processing (ISP) systems dimensionality reduction must go beyond simply fitting the data
geometry. One must account for how dimension reduction will affect the performance of the processing task, e.g., image reconstruction or
classification, and sensor scheduling,
e.g., path planning or waveform design. We will present some thoughts and recent
progress on dimensionality reduction for ISP.