Efficient online model adaptation by incremental simplex Tableau

Zhixian Lei, Xuehan Ye, Yongcai Wang, Deying Li, Jia Xu

Research output: Contribution to conferencePaperpeer-review

3 Scopus citations

Abstract

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 optimal online adaptation. 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 over-fitting especially when the online data is noisy. A Fast-IST approach is therefore proposed, which measures the deviation between the training data and the current model. It schedules updating only when enough deviation 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 better model adaptation speed and accuracy while using even lower computing cost than the state-of-the-art.

Original languageEnglish
Pages2161-2167
Number of pages7
StatePublished - 2017
Event31st AAAI Conference on Artificial Intelligence, AAAI 2017 - San Francisco, United States
Duration: 4 Feb 201710 Feb 2017

Conference

Conference31st AAAI Conference on Artificial Intelligence, AAAI 2017
Country/TerritoryUnited States
CitySan Francisco
Period4/02/1710/02/17

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