Efficient Implementation of a Dimensionality Reduction Method Using a Complex Moment-Based Subspace

Dimensionality reduction methods are widely used for processing data efficiently. Recently Imakura et al. proposed a novel dimensionality reduction method using a complex moment-based subspace. Their method can use more eigenvectors than the existing matrix trace optimization-based methods which explains its reported higher precision. However, the computational complexity is also higher than that of the existing methods, in particular for the nonlinear kernel version. To reduce the computational complexity, we propose a practical parallel implementation of the method by introducing the Nystrom approximation. We evaluate the parallel performance of our implementation using the Oakforest-PACS supercomputer.