Fibonacci heaps and their uses in improved network optimization algorithms
Journal of the ACM (JACM)
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Nonlinear Dimensionality Reduction
Nonlinear Dimensionality Reduction
Semi-supervised geodesic Generative Topographic Mapping
Pattern Recognition Letters
Neural networks and other machine learning methods in cancer research
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Application notes: data mining in cancer research
IEEE Computational Intelligence Magazine
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The diagnostic classification of human brain tumours on the basis of magnetic resonance spectra is a non-trivial problem in which dimensionality reduction is almost mandatory. This may take the form of feature selection or feature extraction. In feature extraction using manifold learning models, multivariate data are described through a low-dimensional manifold embedded in data space. Similarities between points along this manifold are best expressed as geodesic distances or their approximations. These approximations can be computationally intensive, and several alternative software implementations have been recently compared in terms of computation times. The current brief paper extends this research to investigate the comparative ability of dimensionality-reduced data descriptions to accurately classify several types of human brain tumours. The results suggest that the way in which the underlying data manifold is constructed in nonlinear dimensionality reduction methods strongly influences the classification results.