1 research outputs found
Clustering by latent dimensions
This paper introduces a new clustering technique, called {\em dimensional
clustering}, which clusters each data point by its latent {\em pointwise
dimension}, which is a measure of the dimensionality of the data set local to
that point. Pointwise dimension is invariant under a broad class of
transformations. As a result, dimensional clustering can be usefully applied to
a wide range of datasets. Concretely, we present a statistical model which
estimates the pointwise dimension of a dataset around the points in that
dataset using the distance of each point from its nearest
neighbor. We demonstrate the applicability of our technique to the analysis of
dynamical systems, images, and complex human movements.Comment: This paper is submitted to NIPS 2018 conferenc