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Max vs Min: Tensor Decomposition and ICA with nearly Linear Sample Complexity
Rasmus J. Kyng
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Oznake: computer science;machine learning;active learning;computational learning theory;on-line learning;reinforcement learning;semi-supervised learning
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 algorithm for standard ICA with sample complexity nearly linear in the dimension, thereby improving substan ...
Leto: 2015 Vir: videolectures.net
Št. zadetkov: 1
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