期刊
PATTERN RECOGNITION LETTERS
卷 31, 期 12, 页码 1720-1727出版社
ELSEVIER
DOI: 10.1016/j.patrec.2010.05.025
关键词
Dimensionality reduction; Locally Linear Embedding; Isomap; Laplacian Eigenmaps; Mutual information; Radial Basis Function network
资金
- Engineering and Physical Sciences Research Council [EP/E033288/1] Funding Source: researchfish
- EPSRC [EP/E033288/1] Funding Source: UKRI
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. (C) 2010 Elsevier B.V. All rights reserved.
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