The statistical rate for support matrix machines under low rankness and row (column) sparsity-Reference-Cited by-同舟云学术

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The statistical rate for support matrix machines under low rankness and row (column) sparsity

Published:2024-05-24 Issue: Volume: Page:
ISSN:0932-5026
Container-title:Statistical Papers
language:en
Short-container-title:Stat Papers

Author:

Peng Ling^ORCID,Liu Xiaohui,Tan Xiangyong,Zhou Yiweng,Luo Shihua

Funder

National Natural Science Foundation of China

Natural Science Foundation of Jiangxi Province

Key Science and Technology Research Project in Jiangxi Province Department of Education

National Social Science Foundation of China

Outstanding Youth Fund Project of the Science and Technology Department of Jiangxi Province

Postdoctoral Research Foundation of China

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s00362-024-01570-0.pdf

Reference44 articles.

1. Bickel PJ, Ritov Y, Tsybakov AB (2009) Simultaneous analysis of Lasso and Dantzig selector. Annal Stat 37(4):1705–1732. https://doi.org/10.1214/08-AOS620

2. Blanchard G, Bousquet O, Massart P (2008) Statistical performance of support vector machines. Annal Stat pp 489–531

3. Chen J, Ye J (2014) Sparse trace norm regularization. Comput Stat 29(3–4):623–63. https://doi.org/10.1007/s00180-013-0440-7

4. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–29. https://doi.org/10.1007/BF00994018

5. Fan J, Li R (2001) Variable selection via nonconcave penalized likelihood and its oracle properties. J Am Stat Assoc 96(456):1348–136. https://doi.org/10.1198/016214501753382273

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