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Max vs Min: Tensor Decomposition and ICA with nearly Linear Sample Complexity
Published on Aug 20, 20151530 Views
We present a simple, general technique for reducing the sample complexity of matrix and tensor decomposition algorithms applied to distributions. We use the technique to give a polynomial-time algorit
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Chapter list
Max vs Min: Tensor Decomposition and ICA with nearly Linear Sample Complexity00:00
Independent Component Analysis (ICA)00:24
Derivs of Fourier transform01:39
Quallity of eigendecomposition02:36
Our recursive technique03:17