Distributed Incremental wLPSVM Learning

Abstract

Weighted linear proximal support vector machine (wLPSVM) is known as an efficient binary classification algorithm with good accuracy and class-imbalance robustness. In this work, original batch wLPSVM is facilitated with distributed incremental learning capability, which allows simultaneously learning from multiple streaming data sources that are geographically distributed. In our approach, incremental and distributed learning are solved as a merging problem at the same time.

A new wLPSVM expression is derived. In the new expression, knowledge from samples are presented as a set of class-wised core matrices, and merging knowledge from two subsets of data can be simply accomplished by matrix addition. With the new expression, we are able to conduct incremental and distributed learning at the same time via merging knowledge from multiple incremental stages and multiple data sources.