  It is practical to assume that an individual view is unlikely to be
sufficient for effective multi-view learning. Therefore, integration of
multi-view information is both valuable and necessary. In this paper, we
propose the Multi-view Intact Space Learning (MISL) algorithm, which integrates
the encoded complementary information in multiple views to discover a latent
intact representation of the data. Even though each view on its own is
insufficient, we show theoretically that by combing multiple views we can
obtain abundant information for latent intact space learning. Employing the
Cauchy loss (a technique used in statistical learning) as the error measurement
strengthens robustness to outliers. We propose a new definition of multi-view
stability and then derive the generalization error bound based on multi-view
stability and Rademacher complexity, and show that the complementarity between
multiple views is beneficial for the stability and generalization. MISL is
efficiently optimized using a novel Iteratively Reweight Residuals (IRR)
technique, whose convergence is theoretically analyzed. Experiments on
synthetic data and real-world datasets demonstrate that MISL is an effective
and promising algorithm for practical applications.
