期刊
NEURAL NETWORKS
卷 24, 期 5, 页码 501-511出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.neunet.2011.02.007
关键词
Dimensionality reduction; Geodesic distance; Competitive learning; Multidimensional Scaling; Radial Basis Function network
资金
- European Commission [FOOD-CT-2004-513953]
The general purpose dimensionality reduction method should preserve data interrelations at all scales. Additional desired features include online projection of new data, processing nonlinearly embedded manifolds and large amounts of data. The proposed method, called RBF-NDR, combines these features. RBF-NDR is comprised of two modules. The first module learns manifolds by utilizing modified topology representing networks and geodesic distance in data space and approximates sampled or streaming data with a finite set of reference patterns, thus achieving scalability. Using input from the first module, the dimensionality reduction module constructs mappings between observation and target spaces. Introduction of specific loss function and synthesis of the training algorithm for Radial Basis Function network results in global preservation of data structures and online processing of new patterns. The RBF-NDR was applied for feature extraction and visualization and compared with Principal Component Analysis (PCA), neural network for Sammon's projection (SAMANN) and Isomap. With respect to feature extraction, the method outperformed PCA and yielded increased performance of the model describing wastewater treatment process. As for visualization, RBF-NDR produced superior results compared to PCA and SAMANN and matched Isomap. For the Topic Detection and Tracking corpus, the method successfully separated semantically different topics. (C) 2011 Elsevier Ltd. All rights reserved.
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