Accelerating Cross-Validation in Multinomial Logistic Regression with $\ell_1$-Regularization
Tomoyuki Obuchi, Yoshiyuki Kabashima; 19(52):1−30, 2018.
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. An extension to the elastic net regularization is also addressed. The usefulness of the approximate formula is demonstrated on simulated data and the ISOLET dataset from the UCI machine learning repository. MATLAB and python codes implementing the approximate formula are distributed in (Obuchi, 2017; Takahashi and Obuchi, 2017).
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