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2 results for thin-SVD

Develops a method for extracting sources in multi-subject fMRI data.

problem Analyzing brain imaging datasets from multiple subjects with varying levels of jointness.
method Deflation-based algorithm using higher order cumulants and thin-SVD factorization.
result The algorithm accurately identifies joint, partially-joint, and individual sources with high precision.

Composite convex optimization problems which include both a nonsmooth term and a low-rank promoting term have important applications in machine learning and signal processing, such as when one wishes to recover an unknown matrix that is simultaneously low-rank and sparse. However, such problems are highly challenging t…

2018-09-27abs ↗pdf ↗