Error analysis for lq $l^{q}$-coefficient regularized moving least-square regression

Error analysis for lq $l^{q}$-coefficient regularized moving least-square regression

Abstract We consider the moving least-square (MLS) method by the coefficient-based regression framework with lq $l^{q}$-regularizer (1≤q≤2) $(1\leq q\leq2)$ and the sample dependent hypothesis spaces. The data dependent characteristic of the new algorithm provides flexibility and adaptivity for MLS....

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Bibliographic Details
Main Authors: Qin Guo, Peixin Ye
Format: Article
Language:English
Published: SpringerOpen 2018-09-01
Series:Journal of Inequalities and Applications
Subjects:
Moving least-square method
Data dependent hypothesis space
Regularization function
Uniform concentration inequality
Learning rate
Online Access:http://link.springer.com/article/10.1186/s13660-018-1856-y

Internet

http://link.springer.com/article/10.1186/s13660-018-1856-y

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