ABSTRACTS

摘要

We propose an efficient randomized algorithm of spectral clustering. Spectral clustering is a kernel-based method of grouping data on separate nonlinear manifolds. However, its high computational expense restricts the applications. Our algorithm exploits random projection and subsampling techniques for reducing dimensionality and cardinality of data. The computation time can be independent of data dimensionality in appearance-based methods, and is quasilinear with respect to the data cardinality. We demonstrate our spectral clustering algorithm in image and video shot segmentation.