This paper examines the approximation of log-determinant for large-scale symmetric positive definite matrices. Inspired by the variance reduction technique, we split the approximation of $\log\det(A)$ into two parts. The first to compute is the trace of the projection of $\log(A)$ onto a suboptimal subspace, while the second is the trace of the projection on the corresponding orthogonal complementary space. For these two approximations, the stochastic Lanczos quadrature method is used. Furthermore, in the construction of the suboptimal subspace, we utilize a projection-cost-preserving sketch to bound the size of the Gaussian random matrix and the dimension of the suboptimal subspace. We provide a rigorous error analysis for our proposed method and explicit lower bounds for its design parameters, offering guidance for practitioners. We conduct numerical experiments to demonstrate our method's effectiveness and illustrate the quality of the derived bounds.

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