Incremental Nyström Low-Rank Decomposition for Dynamic Learning

Abstract

Eigen-decomposition is a key step in spectral clustering and some kernel methods. The Nyström method is often used to speed up kernel matrix decomposition. However, it cannot effectively update eigenvectors of matrices when datasets dynamically increase with time. In this paper, we propose an incremental Nyström method for dynamic learning. Experimental results demonstrate the feasibility and effectiveness of the proposed method.