
Staging Humancomputer Dialogs: An Application of the Futamura Projections
We demonstrate an application of the Futamura Projections to humancompu...
11/13/2018 ∙ by Brandon M. Williams, et al. ∙ 0 ∙ shareread it

3D Object Classification via Spherical Projections
In this paper, we introduce a new method for classifying 3D objects. Our...
12/12/2017 ∙ by Zhangjie Cao, et al. ∙ 0 ∙ shareread it

Parameter estimation in spherical symmetry groups
This paper considers statistical estimation problems where the probabili...
11/10/2014 ∙ by YuHui Chen, et al. ∙ 0 ∙ shareread it

Gaussian approximation of Gaussian scale mixture
For a given positive random variable V>0 and a given Z∼ N(0,1) independe...
10/04/2018 ∙ by Gérard Letac, et al. ∙ 0 ∙ shareread it

Analysis of SparseHash: an efficient embedding of setsimilarity via sparse projections
Embeddings provide compact representations of signals in order to perfor...
09/02/2019 ∙ by Diego Valsesia, et al. ∙ 0 ∙ shareread it

Learning mixtures of spherical Gaussians: moment methods and spectral decompositions
This work provides a computationally efficient and statistically consist...
06/25/2012 ∙ by Daniel Hsu, et al. ∙ 0 ∙ shareread it

Accounting for Skill in Nonlinear Trend, Variability, and Autocorrelation Facilitates Better MultiModel Projections
We present a novel quasiBayesian method to weight multiple dynamical mo...
11/07/2018 ∙ by Roman Olson, et al. ∙ 0 ∙ shareread it
A concentration theorem for projections
X in R^D has mean zero and finite second moments. We show that there is a precise sense in which almost all linear projections of X into R^d (for d < D) look like a scalemixture of spherical Gaussians  specifically, a mixture of distributions N(0, sigma^2 I_d) where the weight of the particular sigma component is P ( X ^2 = sigma^2 D). The extent of this effect depends upon the ratio of d to D, and upon a particular coefficient of eccentricity of X's distribution. We explore this result in a variety of experiments.
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