3 research outputs found
Quadruply Stochastic Gradient Method for Large Scale Nonlinear Semi-Supervised Ordinal Regression AUC Optimization
Semi-supervised ordinal regression (SOR) 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 SOR AUC
optimization based on ordinal binary decomposition approach. Besides, to handle
the large-scale kernelized learning problems, we propose a scalable algorithm
called QSORAO 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 , where 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.Comment: 12 pages, 9 figures, conferenc