GTM: the generative topographic mapping
Neural Computation
Robust mixture modelling using the t distribution
Statistics and Computing
IEEE Transactions on Knowledge and Data Engineering
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
Nonlinear Dimensionality Reduction
Nonlinear Dimensionality Reduction
Robust analysis of MRS brain tumour data using t-GTM
Neurocomputing
Brain tumor classification based on long echo proton MRS signals
Artificial Intelligence in Medicine
MCPR'11 Proceedings of the Third Mexican conference on Pattern recognition
Expert Systems with Applications: An International Journal
Cartogram visualization for nonlinear manifold learning models
Data Mining and Knowledge Discovery
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Non-invasive techniques such as magnetic resonance spectroscopy (MRS) are often required for assisting the diagnosis of tumours. Radiologists are not always accustomed to make sense of the biochemical information provided by MRS and they may benefit from computer-based support in their decision making. The high dimensionality of the MR spectra obscures atypical aspects of the data that may jeopardize their classification. In this study, we describe a method to overcome this problem that combines nonlinear dimensionality reduction, outlier detection, and expert opinion. MR spectra subsequently undergo a feature selection process followed by classification. The impact of outlier removal on classification performance is assessed.