Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
409904 | Neurocomputing | 2012 | 7 Pages |
Abstract
Based on m randomly drawn vectors in a separable Hilbert space, we investigate the consistency of the regularized regression learning algorithm by using Rademacher averages techniques. Furthermore, random projection technique for speeding up the regression learning algorithm is used. The learning rates of the regularized regression learning algorithm with random projection are established. Theoretical analysis shows that it is possible to learn directly in the projected domain. Our results reflect a tradeoff between accuracy and computational complexity when one uses regularized least square regression algorithm after random projection of the data to a finite dimensional space.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Di-Rong Chen, Han Li,