29 citations · 30 across the 4 of their papers we have counts for
5 papers · 1 filter
Geometric Methods for Robust Data Analysis in High Dimension
Joseph Anderson
Machine learning and data analysis now finds both scientific and industrial application in biology, chemistry, geology, medicine, and physics. These applications rely on large quan…
Heavy-Tailed Analogues of the Covariance Matrix for ICA
Joseph Anderson, Navin Goyal, Anupama Nandi +1
Independent Component Analysis (ICA) is the problem of learning a square matrix , given samples of , where is a random vector with independent coordinates. Most existi…
Heavy-tailed Independent Component Analysis
Joseph Anderson, Navin Goyal, Anupama Nandi +1
Independent component analysis (ICA) is the problem of efficiently recovering a matrix from i.i.d. observations of where …
The More, the Merrier: the Blessing of Dimensionality for Learning Large Gaussian Mixtures
Joseph Anderson, Mikhail Belkin, Navin Goyal +2
In this paper we show that very large mixtures of Gaussians are efficiently learnable in high dimension. More precisely, we prove that a mixture with known identical covariance mat…
Efficient learning of simplices
Joseph Anderson, Navin Goyal, Luis Rademacher
We show an efficient algorithm for the following problem: Given uniformly random points from an arbitrary n-dimensional simplex, estimate the simplex. The size of the sample and th…