Regularized least squares classification or regression with leave-one-out (LOO) error
US7685080B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 27, 2006 |
| Grant date | Mar 23, 2010 |
| Priority date | — |
| Expiry date | Sep 5, 2027 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/10
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Techniques are disclosed that implement algorithms for rapidly finding the leave-one-out (LOO) error for regularized least squares (RLS) problems over a large number of values of the regularization parameter λ. Algorithms implementing the techniques use approximately the same time and space as training a single regularized least squares classifier/regression algorithm. The techniques include a classification/regression process suitable for moderate sized datasets, based on an eigendecomposition of the unregularized kernel matrix. This process is applied to a number of benchmark datasets, to show empirically that accurate classification/regression can be performed using a Gaussian kernel with surprisingly large values of the bandwidth parameter σ. It is further demonstrated how to exploit this large σ regime to obtain a linear-time algorithm, suitable for large datasets, that computes LOO values and sweeps over λ.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.