A framework of regularized low-rank matrix models for regression and classification-Reference-Cited by-同舟云学术

A framework of regularized low-rank matrix models for regression and classification

Published:2023-10-25 Issue:1 Volume:34 Page:
ISSN:0960-3174
Container-title:Statistics and Computing
language:en
Short-container-title:Stat Comput

Author:

Huang Hsin-Hsiung,Yu Feng,Fan Xing,Zhang Teng

Funder

Division of Mathematical Sciences

Division of Computer and Network Systems

Publisher

Springer Science and Business Media LLC

Subject

Computational Theory and Mathematics,Statistics, Probability and Uncertainty,Statistics and Probability,Theoretical Computer Science

Link

https://link.springer.com/content/pdf/10.1007/s11222-023-10318-z.pdf

Reference41 articles.

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3. Beck, A., Teboulle, M.: A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. Imag. Sci. 2(1), 183–202 (2009)

4. Boumal, N.: On intrinsic cramér-rao bounds for Riemannian submanifolds and quotient manifolds. IEEE Trans. Signal Process. 61(7), 1809–1821 (2013). https://doi.org/10.1109/TSP.2013.2242068

5. Boumal, N., Mishra, B., Absil, P.-A., Sepulchre, R.: Manopt, a matlab toolbox for optimization on manifolds. J. Mach. Learn. Res. 15(1), 1455–1459 (2014)

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