Understanding and organizing data, in particular understanding the key modes of variation in the data, is a first
step toward exploiting and evaluating sensor phenomenology. Spectral theory and manifold learning methods
have been recently shown to offer sever powerful tools for many parts of the exploitation problem. We will
describe the method of diffusion maps and give some examples with radar (backhoe data dome) data. The so-called
diffusion coordinates are kernel based dimensionality reduction techniques that can, for example, organize
random data and yield explicit insight into the type and relative importance of the data variation. We will
provide sufficient background for others to adopt these tools and apply them to other aspects of exploitation and
evaluation.
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