Block-quantized kernel matrix for fast spectral embedding

Kai Zhang, James T. Kwok

Abstract: Eigendecomposition of kernel matrix is an indispensable procedure in many learning and vision tasks. However, the cubic complexity $O(N^3)$ is impractical for large problem, where $N$ is the data size. In this paper, we propose an efficient approach to solve the eigendecomposition of the kernel matrix $W$. The idea is to approximate $W$ with $\barW$ that is composed of $m^2$ constant blocks. The eigenvectors of $\barW$, which can be solved in $O(m^3)$ time, is then used to recover the eigenvectors of the original kernel matrix. The complexity of our method is only $O(mN+m^3)$, which scales more favorably than state-of-the-art low rank approximation and sampling based approaches ($O(m^2N+m^3)$), and the approximation quality can be controlled conveniently. Our method demonstrates encouraging scaling behaviors in experiments of image segmentation (by spectral clustering) and kernel principal component analysis.

Proceedings of the Twenty-Third International Conference on Machine Learning (ICML-2006), pp.1097-1104, Pittsburgh, USA, June 2006.


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