Filtering for Markov chains and spectral embedding

Spectral methods have proven to be a valuable tool for the task of mapping a high-dimensional set of points into low dimensions in a way that preserves or even extracts the relevant, underlying geometric structures. We describe a simple operation on Markov chains which can increase the effectivity of these methods and give many examples; we also prove that this method will automatically correct random errors with high probability.