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Time Series Segmentation Using a Novel Adaptive Eigendecomposition Algorithm
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A Novel Measure for Quantifying the Topology Preservation of Self-Organizing Feature Maps
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Piecewise Linear Projection Based on Self-Organizing Map
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Unsupervised feature extraction using neuro-fuzzy approach
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A Kernel Approach to Metric Multidimensional Scaling
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Genetic Algorithms for Exploratory Data Analysis
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Complex Process Visualization through Continuous Feature Maps Using Radial Basis Functions
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Cooperative Information Control to Coordinate Competition and Cooperation
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Correlation Visualization of High Dimensional Data Using Topographic Maps
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Self-organizing maps with recursive neighborhood adaptation
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Fast RLS-Like Algorithm for Generalized Eigendecomposition and its Applications
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IDR/QR: an incremental dimension reduction algorithm via QR decomposition
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IDR/QR: An Incremental Dimension Reduction Algorithm via QR Decomposition
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Pattern Recognition Letters
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FS_SFS: A novel feature selection method for support vector machines
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Classical feature extraction and data projection methods have been well studied in the pattern recognition and exploratory data analysis literature. We propose a number of networks and learning algorithms which provide new or alternative tools for feature extraction and data projection. These networks include a network (SAMANN) for J.W. Sammon's (1969) nonlinear projection, a linear discriminant analysis (LDA) network, a nonlinear discriminant analysis (NDA) network, and a network for nonlinear projection (NP-SOM) based on Kohonen's self-organizing map. A common attribute of these networks is that they all employ adaptive learning algorithms which makes them suitable in some environments where the distribution of patterns in feature space changes with respect to time. The availability of these networks also facilitates hardware implementation of well-known classical feature extraction and projection approaches. Moreover, the SAMANN network offers the generalization ability of projecting new data, which is not present in the original Sammon's projection algorithm; the NDA method and NP-SOM network provide new powerful approaches for visualizing high dimensional data. We evaluate five representative neural networks for feature extraction and data projection based on a visual judgement of the two-dimensional projection maps and three quantitative criteria on eight data sets with various properties