A Matrix Chernoff Bound for Markov Chains and Its Application to Co-occurrence Matrices

Part of Advances in Neural Information Processing Systems 33 (NeurIPS 2020)

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Jiezhong Qiu, Chi Wang, Ben Liao, Richard Peng, Jie Tang


We prove a Chernoff-type bound for sums of matrix-valued random variables sampled via a regular (aperiodic and irreducible) finite Markov chain. Specially, consider a random walk on a regular Markov chain and a Hermitian matrix-valued function on its state space. Our result gives exponentially decreasing bounds on the tail distributions of the extreme eigenvalues of the sample mean matrix. Our proof is based on the matrix expander (regular undirected graph) Chernoff bound [Garg et al. STOC '18] and scalar Chernoff-Hoeffding bounds for Markov chains [Chung et al. STACS '12].

Our matrix Chernoff bound for Markov chains can be applied to analyze the behavior of co-occurrence statistics for sequential data, which have been common and important data signals in machine learning. We show that given a regular Markov chain with n states and mixing time t, we need a trajectory of length O(t(log(n) + log(t))/e^2) to achieve an estimator of the co-occurrence matrix with error bound e. We conduct several experiments and the experimental results are consistent with the exponentially fast convergence rate from theoretical analysis. Our result gives the first bound on the convergence rate of the co-occurrence matrix and the first sample complexity analysis in graph representation learning.