Overcomplete independent component analysis via SDP

Overcomplete independent component analysis via SDP

We present a novel algorithm for overcomplete independent components analysis (ICA), where the number of latent sources k exceeds the dimension p of observed variables. Previous algorithms either suffer from high computational complexity or make strong assumptions about the form of the mixing matrix...

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Bibliographic Details
Main Authors: Podosinnikova, Anastasia, Perry, Amelia E., Wein, Alexander Spence, Sontag, David Alexander
Other Authors: Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Format: Article
Language:English
Published: International Machine Learning Society 2021
Online Access:https://hdl.handle.net/1721.1/130365

Internet

https://hdl.handle.net/1721.1/130365

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