OTLAMC: An Online Transfer Learning Algorithm for Multi-class Classification

Abstract

Transfer learning handles a learning task on target domain by transferring the knowledge which was already learned from source domain, often under the situation where the labeled data in the target domain are insufficient or difficult to acquire. Transfer learning can be generally classified into two types, offline transfer learning and online transfer learning. As the former requires the data of target domain be given in advance, which may not be hold in real-life situations, the latter attracted more and more research in recent years. Online transfer learning deals with the situation where the data of target domain arrive in an online manner. Existing research on online transfer learning only deals with binary classification tasks, which appears to be important insufficiency. In this paper, the problem of online transfer learning for multi-class classification is studied, and an algorithm called Online Transfer Learning Algorithm for Multi-class Classification (OTLAMC) is proposed. OTLAMC learns a multi-class classifier in an online manner based on the knowledge from two sources, the obtained feedback of each datum in the target domain upon its arrival and the knowledge transferred from the source domain. A new loss function and a new updating method are proposed and adopted in OTLAMC, which improved the performance of OTLAMC on multi-class classification task. The mistake bound of OTLAMC is derived. Experiments on two widely used datasets illustrate that OTLAMC has good performance.