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Comparative Diagnostic Accuracy of Linear and Nonlinear Feature Extraction Methods in a Neuro-oncology Problem

机译：线性和非线性特征提取方法在神经肿瘤学中的比较诊断准确性

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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.
机译：基于磁共振波谱对人脑肿瘤的诊断分类是一个非平凡的问题，其中降维几乎是必须的。这可以采取特征选择或特征提取的形式。在使用流形学习模型的特征提取中，通过嵌入数据空间中的低维流形来描述多元数据。沿该流形的点之间的相似性最好用测地距离或其近似表示。这些近似值可能需要大量的计算，并且最近在计算时间方面已经比较了几种替代软件的实现。当前的简要论文扩展了这项研究，以研究降维数据描述对准确分类人类脑肿瘤的几种类型的比较能力。结果表明，非线性降维方法构造基础数据流形的方式对分类结果有很大影响。

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