Oja's algorithm for graph clustering and Markov spectral decomposition

Abstract

Given a positive definite matrix M and an integer Nm ≥ 1, Oja's subspace algorithm will provide convergent estimates of the first Nm eigenvalues of M along with the corresponding eigenvectors. It is a common approach to principal component analysis. This paper introduces a normalized stochastic-appr