Local Online Learning For Large Scale Data

With the development of Internet and smart phone, the speed of data generation from human activities is becoming faster and faster, which is a big challenge in computing.Online learning is very important for processing sequential data and helps alleviate the computation burden on large scale data as well. Especially, one-pass online learning is to predict a new coming sample’s label and update the model based on the prediction, where each coming sample is used only once and never stored. So far,existing one-pass online learning methods are globally modeled and do not take the local structure of data distribution into consideration, which is a signi?cant factor of handling the nonlinear data separation case.In this work, we propose a local online learning(LOL) method, a multiple hyperplane passive aggressive algorithm integrated with online clustering, so that all local hyperplanes are learned jointly and working cooperatively. This is achieved by formulating a common component as information tra?c among multiple hyperplanes in LOL. A joint optimization algorithm is developed and theoretical analysis on the cumulative error is also provided. Extensive experiments on 9 datasets show that LOL can learn a nonlinear decision boundary, overall achieving notably better performance without using any kernel modeling and second order modeling. Our manuscript is currently under second round review for publication on Pattern Recognition(PR).