  In many artificial intelligence and computer vision systems, the same object
can be observed at distinct viewpoints or by diverse sensors, which raises the
challenges for recognizing objects from different, even heterogeneous views.
Multi-view discriminant analysis (MvDA) is an effective multi-view subspace
learning method, which finds a discriminant common subspace by jointly learning
multiple view-specific linear projections for object recognition from multiple
views, in a non-pairwise way. In this paper, we propose the kernel version of
multi-view discriminant analysis, called kernel multi-view discriminant
analysis (KMvDA). To overcome the well-known computational bottleneck of kernel
methods, we also study the performance of using random Fourier features (RFF)
to approximate Gaussian kernels in KMvDA, for large scale learning. Theoretical
analysis on stability of this approximation is developed. We also conduct
experiments on several popular multi-view datasets to illustrate the
effectiveness of our proposed strategy.
