Automatic configuration of spectral dimensionality reduction methods
We propose an advanced framework for the automatic configuration of spectral dimensionality reduction methods. This is achieved by introducing, first, the mutual information measure to assess the quality of discovered embedded spaces. Secondly, unsupervised Radial Basis Function network is designated for mapping between spaces where the learning process is derived from graph theory and based on Markov cluster algorithm. Experiments on synthetic and real datasets demonstrate the effectiveness of the proposed methodology.
dimensionality reduction mutual information radial basis function network locally linear embedding isomap laplacian eigenmaps