Doubly-Stochastic Normalization of the Gaussian Kernel is Robust to Heteroskedastic Noise

31 May 2020 Boris Landa Ronald R. Coifman Yuval Kluger

A fundamental step in many data-analysis techniques is the construction of an affinity matrix describing similarities between data points. When the data points reside in Euclidean space, a widespread approach is to from an affinity matrix by the Gaussian kernel with pairwise distances, and to follow with a certain normalization (e.g. the row-stochastic normalization or its symmetric variant)... (read more)

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