Spectral Clustering with Mean Shift Preprocessing

Clustering is a fundamental problem in machine learning with numerous important applications in statistical signal processing, pattern recognition, and computer vision, where unsupervised analysis of data classification structures are required. The current state-of-the-art in clustering is widely accepted to be the so-called spectral clustering. Spectral clustering, based on pairwise affinities of samples imposes very large computational requirements. In this paper, we propose a vector quantization preprocessing stage for spectral clustering similar to the classical mean-shift principle for clustering. This preprocessing reduces the dimensionality of the matrix on which spectral techniques will be applied, resulting in significant computational savings