Discovering Common Information in Multi-view Data

Another grounded in the consensus principle method draws on The advent of diverse and heterogeneous data due to recent mutual information in information theory. The authors in [17] technological advancements has spurred increasing interest in posit that each view contains identical task-relevant information, multi-view learning [1, 2, 3]. This field relies on two principles: a classic hypothesis suggesting that effective representation the consensus principle, which seeks consensus information models view-invariant factors. They develop robust representations across different views, and the complementary principle, by maximizing the mutual information between representations which recognizes the unique, valuable information each view from different views. A similar approach is used offers [4, 5, 6]. For instance, consider the case of an animal's in [18], where information about high-level factors that span binocular vision. Each eye captures a different yet highly correlated across multiple views is captured by maximizing the mutual information perspective of an object, extracting consensus information between the extracted features.