A Minimax Lower Bound for Low-Rank Matrix-Variate Logistic Regression
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This paper considers the problem of matrix-variate logistic regression. The fundamental error threshold on estimating coefficient matrices in the logistic regression problem is found by deriving a lower bound on the minimax risk. The focus of this paper is on derivation of a minimax risk lower bound for low-rank coefficient matrices. The bound depends explicitly on the dimensions and distribution of the covariates, the rank and energy of the coefficient matrix, and the number of samples. The resulting bound is proportional to the intrinsic degrees of freedom in the problem, which suggests the sample complexity of the low-rank matrix logistic regression problem can be lower than that for vectorized logistic regression. The proof techniques utilized in this work also set the stage for development of minimax lower bounds for tensor-variate logistic regression problems.
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