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Online Learning Based on Online DCA and Application to Online Classification.
Hoai An Le Thi1, Vinh Thanh Ho2
1LGIPM, University of Lorraine, F 57000 Metz, France hoai-an.le-thi@univ-lorraine.fr.
Neural Computation
|February 19, 2020
Summary
This study introduces Difference of Convex functions (DC) programming and the DC Algorithm (DCA) for online learning. The proposed online DCA algorithms demonstrate efficiency in binary linear classification tasks, achieving competitive results on benchmark datasets.
Area of Science:
- Machine Learning
- Optimization
- Computer Science
Background:
- Online learning algorithms are crucial for sequential data prediction.
- Difference of Convex (DC) programming offers a framework for complex optimization problems.
- Existing online learning methods may face challenges with non-convex objectives.
Discussion:
- This research formulates online learning prediction as a DC program.
- It applies the DC Algorithm (DCA) for online prediction.
- Two versions of the online DCA scheme (complete/approximate) are proposed and analyzed.
Key Insights:
- The proposed online DCA schemes achieve logarithmic/sublinear regrets.
- Six distinct online DCA-based algorithms are developed for binary linear classification.
- Numerical experiments validate the efficiency of these algorithms against state-of-the-art methods.
Outlook:
- Further exploration of DC programming in diverse online learning tasks.
- Potential for developing more sophisticated online DCA variants.
- Application of these algorithms to larger and more complex datasets.
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