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Statistics and Data Science Seminar Series Hariharan Narayanan

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Fitting a putative manifold to noisy data

Hariharan Narayanan (Tata Institute of Fundamental Research, Mumbai)
E18-304

Abstract: We give a solution to the following question from manifold learning. Suppose data belonging to a high dimensional Euclidean space is drawn independently, identically distributed from a measure supported on a low dimensional twice differentiable embedded compact manifold M, and is corrupted by a small amount of i.i.d gaussian noise. How can we produce…

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