Multi-view Privileged Information-based Representation Learning for Liver Cancer Diagnosis

Bangming Gong, Huo Yan

Abstract: Privileged information (PI) provides additional knowledge to improve performance. Though some efforts are carried out by learning using privileged information (LUPI), they mainly focus on classifier-level LUPI and single-view PI tasks. Therefore, it is a challenge for feature representation learning by transferring multi-view PI to improve the main view. In this paper, we propose a novel feature-level LUPI for multi-view PI tasks, called the multi-view privileged information-based representation learning (MPIRL) algorithm, in which multi-view PI and main view are required at the training phase, but only the main view is available at the testing phase. MPIRL consists of a feature-level LUPI module and a classification module. The feature-level LUPI module of MPIRL designs a multi-branch structure to transfer the multi-view privileged information to the main view, so that diversity and discriminative representation can be generated. For the classification module, multi-view deep SVM (MDSVM) is developed, which combines a multi-channel deep neural network with SVM into a unified framework. MDSVM further learns the fusion representation and classification simultaneously to improve the generalization performance. The experimental results on the dual-view PI tasks and multi-view PI tasks of the real-world multi-view liver cancer dataset show that the proposed MPIRL achieves superior performance with an accuracy of 86.92%, sensitivity of 89.58%, and specificity of 84.25%.

Submission Number: 223