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To build an effective dimensionality reduction model usually requires sufficient data. Otherwise, traditional dimensionality reduction methods might be less effective. However, sufficient data cannot always be guaranteed in real applications. In this paper we focus on performing unsupervised dimensionality reduction on the high-dimensional and small-sized data, in which the dimensionality of target...
In this paper, we propose a novel method named Mixed Kernel CCA (MKCCA) to achieve easy yet accurate implementation of dimensionality reduction. MKCCA consists of two major steps. First, the high dimensional data space is mapped into the reproducing kernel Hilbert space (RKHS) rather than the Hilbert space, with a mixture of kernels, i.e. a linear combination between a local kernel and a global kernel...
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