  Conventional vision algorithms adopt a single type of feature or a simple
concatenation of multiple features, which is always represented in a
high-dimensional space. In this paper, we propose a novel unsupervised spectral
embedding algorithm called Kernelized Multiview Projection (KMP) to better fuse
and embed different feature representations. Computing the kernel matrices from
different features/views, KMP can encode them with the corresponding weights to
achieve a low-dimensional and semantically meaningful subspace where the
distribution of each view is sufficiently smooth and discriminative. More
crucially, KMP is linear for the reproducing kernel Hilbert space (RKHS) and
solves the out-of-sample problem, which allows it to be competent for various
practical applications. Extensive experiments on three popular image datasets
demonstrate the effectiveness of our multiview embedding algorithm.
