One-pass online learning: A local approach

Zhaoze Zhou, Wei Shi Zheng, Jian Fang Hu, Yong Xu, Jia You

Research output: Journal article publicationJournal articleAcademic researchpeer-review

19 Citations (Scopus)


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 the data distribution into consideration, which is a significant 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 traffic among multiple hyperplanes in LOL. A joint optimization algorithm is proposed and theoretical analysis on the cumulative error is also provided. Extensive experiments on 11 datasets show that LOL can learn a nonlinear decision boundary, overall achieving notably better performance without using any kernel modeling and second order modeling.
Original languageEnglish
Pages (from-to)346-357
Number of pages12
JournalPattern Recognition
Publication statusPublished - 1 Mar 2016


  • Classification
  • Local modeling
  • One-pass online learning

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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