Efficient Online Model Adaptation by Incremental Simplex Tableau

Online multi-kernel learning is promising in the era of mobile computing, in which a combined classifier with multiple kernels are offline trained, and online adapts to personalized features for serving the end user precisely and smartly. The online adaptation is mainly carried out at the end-devices, which requires the adaptation algorithms to be light, efficient and accurate. Previous results focused mainly on efficiency. This paper proposes an novel online model adaptation framework for not only efficiency but also speedy and online adaptation optimality. At first, an online optimal incremental simplex tableau (IST) algorithm is proposed, which approaches the model adaption by linear programming and produces the optimized model update in each step when a personalized training data is collected. But keeping online optimal in each step is expensive and may cause overfitting especially when the online data is noisy. A Fast-IST approach is therefore proposed, which measures the derivation between the training data and the current model. It schedules updating only when enough derivation is detected. The efficiency of each update is further enhanced by running IST only limited iterations, which bounds the computation complexity. Theoretical analysis and extensive evaluations show that Fast-IST saves computation cost greatly, while achieving speedy and accurate model adaptation. It provides much better model adaptation speed and accuracy while using even lower computing cost than the stateof-the-art.