29 citations · 106 across the 19 of their papers we have counts for
10 papers · 1 filter
Overcomplete order-3 tensor decomposition, blind deconvolution and Gaussian mixture models
Haolin Chen, Luis Rademacher
We propose a new algorithm for tensor decomposition, based on Jennrich's algorithm, and apply our new algorithmic ideas to blind deconvolution and Gaussian mixture models. Our firs…
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 …
A Pseudo-Euclidean Iteration for Optimal Recovery in Noisy ICA
James Voss, Mikhail Belkin, Luis Rademacher
Independent Component Analysis (ICA) is a popular model for blind signal separation. The ICA model assumes that a number of independent source signals are linearly mixed to form th…
Eigenvectors of Orthogonally Decomposable Functions
Mikhail Belkin, Luis Rademacher, James Voss
The Eigendecomposition of quadratic forms (symmetric matrices) guaranteed by the spectral theorem is a foundational result in applied mathematics. Motivated by a shared structure f…
The Hidden Convexity of Spectral Clustering
James Voss, Mikhail Belkin, Luis Rademacher
In recent years, spectral clustering has become a standard method for data analysis used in a broad range of applications. In this paper we propose a new class of algorithms for mu…