A non-asymptotic study of low-rank estimation of smooth kernels on graphs
This dissertation investigates the problem of estimating a kernel over a large graph based on a sample of noisy observations of linear measurements of the kernel. We are interested in solving this estimation problem in the case when the sample size is much smaller than the ambient dimension of the k...
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| Format: | Others |
| Language: | en_US |
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Georgia Institute of Technology
2015
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| Online Access: | http://hdl.handle.net/1853/52988 |