Search Results for author:
Found 11 papers, 1 papers with code
On the Nystrom Approximation for Preconditioning in Kernel Machines
Scalable algorithms for learning kernel models need to be iterative in nature, but convergence can be slow due to poor conditioning.
Overcomplete order-3 tensor decomposition, blind deconvolution and Gaussian mixture models
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.
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.
A Pseudo-Euclidean Iteration for Optimal Recovery in Noisy ICA
We propose a new algorithm, PEGI (for pseudo-Euclidean Gradient Iteration), for provable model recovery for ICA with Gaussian noise.
Eigenvectors of Orthogonally Decomposable Functions
It includes influential Machine Learning methods such as cumulant-based FastICA and the tensor power iteration for orthogonally decomposable tensors as special cases.
The Hidden Convexity of Spectral Clustering
Geometrically, the proposed algorithms can be interpreted as hidden basis recovery by means of function optimization.
Fast Algorithms for Gaussian Noise Invariant Independent Component Analysis
In our paper we develop the first practical algorithm for Independent Component Analysis that is provably invariant under Gaussian noise.
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.
Blind Signal Separation in the Presence of Gaussian Noise
In this paper we propose a new algorithm for solving the blind signal separation problem in the presence of additive Gaussian noise, when we are given samples from X=AS+\eta, where \eta is drawn from an unknown, not necessarily spherical n-dimensional Gaussian distribution.
