On the limited memory BFGS method for large scale optimization
Mathematical Programming: Series A and B
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
Comment on ``A Nonlinear Mapping for Data Structure Analysis''
IEEE Transactions on Computers
Decision boundary feature extraction for neural networks
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
A DTW-based probability model for speaker feature analysis and data mining
Pattern Recognition Letters
High speed associative memories for feature extraction and visualisation
Pattern Recognition Letters
Making every bit count: fast nonlinear axis scaling
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A new pixel-oriented visualization technique through color image
Information Visualization
A local semi-supervised Sammon algorithm for textual data visualization
Journal of Intelligent Information Systems
Efficient locally linear embeddings of imperfect manifolds
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
A comparison of dimensionality reduction methods using topology preservation indexes
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
Stability of dimensionality reduction methods applied on artificial hyperspectral images
ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
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Dimensionality reduction techniques have been regularly used for visualization of high-dimensional data sets. In this paper, reduction to d = 2 is studied, with the purpose of feature extraction. Four different non-linear techniques are studied: multidimensional scaling, Sammon's mapping, self-organizing maps and auto-associative feedforward networks. All four techniques will be presented in the same framework of optimization. A comparison with respect to feature extraction is made by evaluating the reduced feature sets ability to perform classification tasks. The experiments involve an artificial data set and grey-level and color texture data sets. We demonstrate the usefulness of non-linear techniques compared to linear feature extraction.