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Globally Convergent Block Coordinate Descent Algorithms for Semi-Nonnegative Independent Component Analysis
We propose a subgradient-based block coordinate descent (BCD-SubG) framework for nonconvex optimization over products of probability simplices and a closed feasible set, motivated by semi-nonnegative independent component analysis (Semi-NNICA). Elementary column transformations are used to preserve
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