Subspace Learning Augmented with Class Conditional Probability Estimation Based on SVM Classifier in Domain Adaptation

The rapid evolution of data has challenged traditional machine learning methods and leads to the failure of many learning models. As a possible solution to the lack of sufficient labeled data, transfer learning aims to exploit the accumulated knowledge in an auxiliary domain to develop new predictive models. This article studies a specific type of transfer learning called domain adaptation, which works based on subspace learning in order to minimize distance between class conditional probability distributions of source and target domains and to preserve source discriminative information. SVM classifier trained on source domain data has been used to predict target domain data labels to facilitate subspace learning. In this work, subspace learning is formulated as an optimization problem and experiments have been carried out on the real- world datasets. The results of experiments indicate that the proposed method outperforms several exiting methods at this field in the term of accuracy in two object recognition benchmarks: Offlce-Caltech10 and Office datasets.