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Chen, Di-Rong; Li, Han

The coefficient regularized regression with random projection. (English) Zbl 1248.68400

Int. J. Wavelets Multiresolut. Inf. Process. 10, No. 2, Paper No. 12, 13 p. (2012).
Summary: In this paper, a coefficient regularized regression algorithm with random projection is proposed. The excess error of the proposed algorithm associated with the reproducing kernel Hilbert space is bounded. Theoretical analysis shows that it is possible to learn directly in the projected domain and that random projection, as a preprocessing step in supervised learning, leads to a computationally simple empirical risk minimization on the projected data.

Cited in 1 Document

MSC:

68T05 Learning and adaptive systems in artificial intelligence
62J02 General nonlinear regression

Keywords:

regularized least square regression; random projection; reproducing kernel Hilbert spaces; learning rate; coefficient regularization
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References:

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