  Existing multi-view learning methods based on kernel function either require
the user to select and tune a single predefined kernel or have to compute and
store many Gram matrices to perform multiple kernel learning. Apart from the
huge consumption of manpower, computation and memory resources, most of these
models seek point estimation of their parameters, and are prone to overfitting
to small training data. This paper presents an adaptive kernel nonlinear
max-margin multi-view learning model under the Bayesian framework.
Specifically, we regularize the posterior of an efficient multi-view latent
variable model by explicitly mapping the latent representations extracted from
multiple data views to a random Fourier feature space where max-margin
classification constraints are imposed. Assuming these random features are
drawn from Dirichlet process Gaussian mixtures, we can adaptively learn
shift-invariant kernels from data according to Bochners theorem. For inference,
we employ the data augmentation idea for hinge loss, and design an efficient
gradient-based MCMC sampler in the augmented space. Having no need to compute
the Gram matrix, our algorithm scales linearly with the size of training set.
Extensive experiments on real-world datasets demonstrate that our method has
superior performance.
