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Comparative diagnostic accuracy of linear and nonlinear feature extraction methods in a neuro-oncology problem

機(jī)譯：線性和非線性特征提取方法在神經(jīng)腫瘤學(xué)問題中的比較診斷準(zhǔn)確性

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摘要

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.
機(jī)譯：基于磁共振波譜對人腦腫瘤的診斷分類是一個非平凡的問題，其中降維幾乎是必須的。這可以采取特征選擇或特征提取的形式。在使用流形學(xué)習(xí)模型的特征提取中，通過嵌入數(shù)據(jù)空間中的低維流形來描述多元數(shù)據(jù)。沿該流形的點(diǎn)之間的相似性最好用測地距離或其近似表示。這些近似值可能需要大量的計(jì)算，并且最近在計(jì)算時間方面已經(jīng)比較了幾種替代軟件的實(shí)現(xiàn)。當(dāng)前的簡要論文擴(kuò)展了這項(xiàng)研究，以研究降維數(shù)據(jù)描述對準(zhǔn)確分類人類腦腫瘤的幾種類型的比較能力。結(jié)果表明，非線性降維方法構(gòu)造基礎(chǔ)數(shù)據(jù)流形的方式對分類結(jié)果有很大影響。

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