Exploiting low-rank approximations of kernel matrics in denoising applicationS

Teixeira, Ana; Tomé, A. M.; Lang, E.W.

http://hdl.handle.net/10400.26/47374

Use this identifier to reference this record.

Name:	Description:	Size:	Format:
Exploiting low-rank approximations of kernel matrices in denoising applications..pdf		998.73 KB	Adobe PDF	Download

Send Feedback

Authors

Teixeira, Ana

Tomé, A. M.

Lang, E.W.

Abstract(s)

The eigendecomposition of a kernel matrix can present a computational burden in many kernel methods. Nevertheless only the largest eigenvalues and corresponding eigenvectors need to be computed. In this work we discuss the Nystrom low-rank approximations of the kernel matrix and its applications in KPCA denoising tasks. Furthermore, the low-rank approximations have the advantage of being related with a smaller subset of the training data which constitute then a basis of a subspace. In a common algebraic framework we discuss the different approaches to compute the basis. Numerical simulations concerning the denoising are presented to compare the discussed approaches.