A Subspace Transfer Learning Algorithm Integrating Heterogeneous Features
Jing Zhang · Acta Automatica Sinica · 2014
The traditional transfer feature algorithms usually focus on learning by using the common features between the source domain and the target domain but ignore the discriminant information of the specific features of each domain,which makes the existing algorithms lack the adaptability to some extent. In order to circumvent this issue, in this paper a novel subspace transfer learning algorithm integrating heterogeneous features(STL-IHF) is proposed based on the empirical risk minimum framework. The proposed method is based on the support vector machine(SVM)-like framework with the feature space of each domain as a combination of the common features and the specified features. The proposed algorithm can not only realize the transfer learning from the common features but also effectively leverage the specified features of each domain, which makes it have much better adaptability in learning. Experimental results on simulation and real data set show the power of the proposed algorithm.