Regularized Nyström Subsampling in Covariate Shift Domain Adaptation Problems

Abstract

The unsupervised domain adaptation problem with covariate shift assumption is considered. Within the framework of the Reproducing Kernel Hilbert Space concept, an algorithm is constructed that is a combination of the Nyström subsampling and the iterated Tikhonov regularization. This approach allows significantly reduce the amount of computing resources involved and at the same time achieves the minimal (by order) approximation accuracy under the big data settings.

Keywords:

• Big data
• computational complexity
• Nyström subsampling
• Radon-Nikodym derivative
• regularization
• source condition
• unsupervised domain adaptation

Additional information

Funding

The authors acknowledge partial financial support due to the project “Mathematical modeling of complex dynamical systems and processes caused by the state security” (Reg. No. 0123U100853).