  This paper derives exponential tail bounds and polynomial moment inequalities
for the spectral norm deviation of a random matrix from its mean value. The
argument depends on a matrix extension of Stein's method of exchangeable pairs
for concentration of measure, as introduced by Chatterjee. Recent work of
Mackey et al. uses these techniques to analyze random matrices with additive
structure, while the enhancements in this paper cover a wider class of
matrix-valued random elements. In particular, these ideas lead to a bounded
differences inequality that applies to random matrices constructed from weakly
dependent random variables. The proofs require novel trace inequalities that
may be of independent interest.
