Online Semi-Supervised Learning With Multiple Regularization Terms

Abstract

Online semi-supervised learning (OS 2 L) has received much attention recently because of its well practical usefulness. Most of the existing studies of OS 2 L are related to manifold regularization. In this paper, we introduce a novel OS 2 L framework with multiple regularization terms based on the notion of ascending the dual function in constrained optimization. Using the Fenchel conjugate, different semisupervised regularization terms can be integrated into the dual function easily and directly. This approach is derived by updating limited dual coefficient variables on each learning round. To be practical, we also employ buffering strategies and sparse approximation approaches in this paper. The experimental studies show that our methods achieve accuracy comparable to offline algorithms while consuming less time and memory. Especially, our OS 2 L algorithms can handle the settings where the target hyperplane of classification continually drifts with the sequence of arriving instances. This paper paves a way to design and analyze OS 2 L algorithms with multiple regularization terms. INDEX TERMSOnline semi-supervised learning (OS 2 L), SVM, manifold regularization, co-regularization, Fenchel conjugate.

Parallel tracking and detection for long-term object tracking

“…24 Ding et al 25 proposed a novel manifold regularized model in a reproducing kernel Hilbert space to solve the online semi-supervised learning. Chen et al 26 proposed a novel online semi-supervised learning framework which exploited multiple regularization terms based on the notion of ascending the dual function in constrained optimization. Liu et al 27 proposed an adaptive and online semi-supervised least square SVM with a manifold regularization for the online classification of streaming data.…”