  Multi-view datasets are increasingly collected in many real-world
applications, and we have seen better learning performance by existing
multi-view learning methods than by conventional single-view learning methods
applied to each view individually. But, most of these multi-view learning
methods are built on the assumption that at each instance no view is missing
and all data points from all views must be perfectly paired. Hence they cannot
handle unpaired data but ignore them completely from their learning process.
However, unpaired data can be more abundant in reality than paired ones and
simply ignoring all unpaired data incur tremendous waste in resources. In this
paper, we focus on learning uncorrelated features by semi-paired subspace
learning, motivated by many existing works that show great successes of
learning uncorrelated features. Specifically, we propose a generalized
uncorrelated multi-view subspace learning framework, which can naturally
integrate many proven learning criteria on the semi-paired data. To showcase
the flexibility of the framework, we instantiate five new semi-paired models
for both unsupervised and semi-supervised learning. We also design a successive
alternating approximation (SAA) method to solve the resulting optimization
problem and the method can be combined with the powerful Krylov subspace
projection technique if needed. Extensive experimental results on multi-view
feature extraction and multi-modality classification show that our proposed
models perform competitively to or better than the baselines.
