Boosting One-Class Transfer Learning for Multiple View Uncertain Data

Transfer learning can leverage knowledge from source tasks to improve learning on a target task, even when training samples are limited. However, most previous transfer learning approaches focus on a single view of the data and assume no uncertainty in the training samples. To address these limitations, we propose a novel method called boosting one-class transfer learning for multi-view uncertain data (UMTO-SVMs), which handles one-class classification in multi-view data with uncertain information. Our method transfers knowledge containing uncertainty from multiple source tasks to the target task and constrains complementary information across different views to improve consistency. By combining basic classifiers using the Adaboost algorithm, we build a robust classifier. We also design an iterative framework to optimize the method and prove the convergence of the algorithm. Experimental results on three benchmark datasets show that UMTO-SVMs outperform previous one-class classification methods.