Rank-consistency-based multi-view learning with Universum

Abstract

In multi-view learning field, preserving data privacy is an important topic and a good solution is rank-consistency-based multi-view learning (RANC). RANC exploits view relationship and preserves data privacy simultaneously and related experiments also validate that RANC improves the individual view-specific learners with the usage of information from other views and parts of features. While performance of RANC is still limited by the insufficient of prior knowledge. Thus we introduce Universum learning into RANC to create additional unlabeled instances which provide more useful prior knowledge. The developed RANC with Universum learning is abbreviated to RANCU. Related experiments on some multi-view data sets have validated the performance of our RANCU theoretically and empirically.