Multi-View Representation Learning via View-Aware Modulation

Multi-view (representation) learning derives an entity's representation from its multiple observable views to facilitate various downstream tasks. The most challenging topic is how to model unobserved entities and their relationships to specific views. To this end, this work proposes a novel multi-view learning method using a View-Aware parameter Modulation mechanism, termed VAM. The key idea is to use trainable parameters as proxies for unobserved entities and views, such that modeling entity-view relationships is converted into modeling the relationship between proxy parameters. Specifically, we first build a set of trainable parameters to learn a mapping from multi-view data to the unified representation as the entity proxy. Then we learn a prototype for each view and design a Modulation Parameter Generator (MPG) that learns a set of view-aware scale and shift parameters from prototypes to modulate the entity proxy and obtain view proxies. By constraining the representativeness, uniqueness, and simplicity of the proxies and proposing an entity-view contrastive loss, parameters are alternatively updated. We end up with a set of discriminative prototypes, view proxies, and an entity proxy that are flexible enough to yield robust representations for out-of-sample entities. Extensive experiments on five datasets show that the results of our VAM outperform existing methods in both classification and clustering tasks.