  Over recent decades have witnessed considerable progress in whether
multi-task learning or multi-view learning, but the situation that consider
both learning scenes simultaneously has received not too much attention. How to
utilize multiple views latent representation of each single task to improve
each learning task performance is a challenge problem. Based on this, we
proposed a novel semi-supervised algorithm, termed as Multi-Task Multi-View
learning based on Common and Special Features (MTMVCSF). In general,
multi-views are the different aspects of an object and every view includes the
underlying common or special information of this object. As a consequence, we
will mine multiple views jointly latent factor of each learning task which
consists of each view special feature and the common feature of all views. By
this way, the original multi-task multi-view data has degenerated into
multi-task data, and exploring the correlations among multiple tasks enables to
make an improvement on the performance of learning algorithm. Another obvious
advantage of this approach is that we get latent representation of the set of
unlabeled instances by the constraint of regression task with labeled
instances. The performance of classification and semi-supervised clustering
task in these latent representations perform obviously better than it in raw
data. Furthermore, an anti-noise multi-task multi-view algorithm called
AN-MTMVCSF is proposed, which has a strong adaptability to noise labels. The
effectiveness of these algorithms is proved by a series of well-designed
experiments on both real world and synthetic data.
