  Although multi-view learning has made signifificant progress over the past
few decades, it is still challenging due to the diffificulty in modeling
complex correlations among different views, especially under the context of
view missing. To address the challenge, we propose a novel framework termed
Cross Partial Multi-View Networks (CPM-Nets), which aims to fully and
flflexibly take advantage of multiple partial views. We fifirst provide a
formal defifinition of completeness and versatility for multi-view
representation and then theoretically prove the versatility of the learned
latent representations. For completeness, the task of learning latent
multi-view representation is specififically translated to a degradation process
by mimicking data transmission, such that the optimal tradeoff between
consistency and complementarity across different views can be implicitly
achieved. Equipped with adversarial strategy, our model stably imputes missing
views, encoding information from all views for each sample to be encoded into
latent representation to further enhance the completeness. Furthermore, a
nonparametric classifification loss is introduced to produce structured
representations and prevent overfifitting, which endows the algorithm with
promising generalization under view-missing cases. Extensive experimental
results validate the effectiveness of our algorithm over existing state of the
arts for classifification, representation learning and data imputation.
