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Found 5 papers, 0 papers with code
Geometric Methods for Robust Data Analysis in High Dimension
Our algorithm again transforms samples from a Gaussian mixture model into samples which fit into the ICA model and, when processed by an ICA algorithm, result in recovery of the mixture parameters.
Heavy-Tailed Analogues of the Covariance Matrix for ICA
Like the current state-of-the-art, the new algorithm is based on the centroid body (a first moment analogue of the covariance matrix).
Heavy-tailed Independent Component Analysis
Independent component analysis (ICA) is the problem of efficiently recovering a matrix $A \in \mathbb{R}^{n\times n}$ from i. i. d.
The More, the Merrier: the Blessing of Dimensionality for Learning Large Gaussian Mixtures
The problem of learning this map can be efficiently solved using some recent results on tensor decompositions and Independent Component Analysis (ICA), thus giving an algorithm for recovering the mixture.
Efficient learning of simplices
We also show a direct connection between the problem of learning a simplex and ICA: a simple randomized reduction to ICA from the problem of learning a simplex.
