Use the uncentered sample covariance matrixThen and, simultaneously for every unit vector ,Here and the effective rank of a covariance matrix satisfiesThe Gaussian sample-covariance operator-norm bound therefore gives, with probability at least ,Under the assumed upper bound on , the second term is at most the first. Thus the stronger simultaneous estimateholds for every . Squaring and using that the displayed ratio is at most one gives the inequality requested in the question after enlarging the universal constant .
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