A novel unsupervised Globality-Locality Preserving Projections in transfer learning

Abstract

In this paper, a novel unsupervised dimensionality reduction algorithm, unsupervised Globality-Locality Preserving Projections in Transfer Learning (UGLPTL) is proposed, based on the conventional Globality-Locality Preserving dimensionality reduction algorithm (GLPP) that does not work well in real-world Transfer Learning (TL) applications. In TL applications, one application (source domain) contains sufficient labeled data, but the related application contains only unlabeled data (target domain). Compared to the existing TL methods, our proposed method incorporates all the objectives, such as minimizing the marginal and conditional distributions between both the domains, maximizing the variance of the target domain, and performing Geometrical Diffusion on Manifolds, all of which are essential for transfer learning applications. UGLPTL seeks a projection vector that projects the source and the target domains data into a common subspace where both the labeled source data and the unlabeled target data can be utilized to perform dimensionality reduction. Comprehensive experiments have verified that the proposed method outperforms many state-of-the-art non-transfer learning and transfer learning methods on two popular real-world cross-domain visual transfer learning data sets. Our proposed UGLPTL approach achieved 82.18% and 87.14% mean accuracies over all the tasks of PIE Face and Office-Caltech data sets, respectively.