Topology representing networks
Neural Networks
Principal component neural networks: theory and applications
Principal component neural networks: theory and applications
A fast fixed-point algorithm for independent component analysis
Neural Computation
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Unsupervised learning in neural computation
Theoretical Computer Science - Natural computing
Nonlinear Projection with the Isotop Method
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
ESOM: An Algorithm to Evolve Self-Organizing Maps from On-Line Data Streams
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
Document Clustering Using Locality Preserving Indexing
IEEE Transactions on Knowledge and Data Engineering
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
Nonlinear projection using geodesic distances and the neural gas network
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Curvilinear component analysis: a self-organizing neural network for nonlinear mapping of data sets
IEEE Transactions on Neural Networks
Artificial neural networks for feature extraction and multivariate data projection
IEEE Transactions on Neural Networks
A nonlinear projection method based on Kohonen's topology preserving maps
IEEE Transactions on Neural Networks
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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.