Feb 17, 2018 · Our approach is to directly estimate the low-rank factor by minimizing a nonconvex quadratic loss function via vanilla gradient descent, ...
Our approach is to directly estimate the low-rank factor by minimizing a nonconvex least-squares loss function via vanilla gradient descent, following a ...
This feature allows one to employ much more aggressive step sizes compared with the ones suggested in prior literature, without the need of sample splitting.
[PDF] Nonconvex Matrix Factorization from Rank-One Measurements
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This feature allows one to employ much more aggressive step sizes compared with the ones suggested in prior literature, without the need of sample splitting. 1 ...
This work considers the problem of recovering low-rank matrices from random rank-one measurements, which spans numerous applications including covariance ...
Our approach is to directly estimate the low-rank factor by minimizing a nonconvex quadratic loss function via vanilla gradient descent, following a tailored ...
Mar 1, 2021 · Our approach is to directly estimate the low-rank factor by minimizing a nonconvex least-squares loss function via vanilla gradient descent, ...
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We study the low rank matrix factorization problem via nonconvex optimization. Com- pared with the convex relaxation approach, nonconvex optimization ...
The non-convex bilinear low-rank matrix factorization (NCBF) model is proposed. An optimization algorithm is also proposed that combines the three strategies of ...
Missing: Measurements. | Show results with:Measurements.
This tutorial-style overview highlights the important role of statistical models in enabling efficient nonconvex optimization with performance guarantees ...