Accelerating Cross-Validation in Multinomial Logistic Regression with $\ell_1$-Regularization.
We develop an approximate formula for evaluating a cross-validation estimator of predictive likelihood for multinomial logistic regression regularized by an $\ell_1$-norm. This allows us to avoid repeated optimizations required for literally conducting cross-validation; hence, the computational time can be significantly reduced. The formula is derived through a perturbative approach employing the largeness of the data size and the model dimensionality. Its usefulness is demonstrated on simulated data and the ISOLET dataset from the UCI machine learning repository.
Publisher URL: http://arxiv.org/abs/1711.05420
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