Quadruply Stochastic Gradient Method for Large Scale Nonlinear Semi Supervised Ordinal Regression AUC Optimization
Semi supervised ordinal regression (S$^2$OR) problems are ubiquitous in real world applications, where only a few ordered instances are labeled and massive instances remain unlabeled. Recent researches have shown that directly optimizing concordance index or AUC can impose a better ranking on the data than optimizing the traditional error rate in ordinal regression (OR) problems. In this paper, we propose an unbiased objective function for S$^2$OR AUC optimization based on ordinal binary decomposition approach. Besides, to handle the large scale kernelized learning problems, we propose a scalable algorithm called QS$^3$ORAO using the doubly stochastic gradients (DSG) framework for functional optimization. Theoretically, we prove that our method can converge to the optimal solution at the rate of $O(1/t) where $t$ is the number of iterations for stochastic data sampling. Extensive experimental results on various benchmark and real world datasets also demonstrate that our method is efficient and effective while retaining similar generalization performance.
AAAI