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Found 5 papers, 1 papers with code
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.
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.
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.
