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首页> 外文期刊>Journal of biomedical informatics. >Direct classification of high-dimensional data in low-dimensional projected feature spaces--comparison of several classification methodologies.
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Direct classification of high-dimensional data in low-dimensional projected feature spaces--comparison of several classification methodologies.

机译：在低维投影特征空间中对高维数据进行直接分类-几种分类方法的比较。

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摘要

Previously, we introduced a distance (similarity)-based mapping for the visualization of high-dimensional patterns and their relative relationships. The mapping preserves exactly the original distances from all points to any two reference patterns in a special two-dimensional coordinate system, the relative distance plane (RDP). We extend the RDP mapping's applicability from visualization to classification. Several of the classifiers use the RDP directly. These include the standard linear discriminant analysis (LDA), nearest neighbor classifiers, and a transvariation probabilities-based classification method that is natural in the RDP. Several reference directions can also be combined to create new coordinate systems in which arbitrary classifiers can be developed. We obtain increased confidence in the classification results by cycling through all possible reference pairs and computing a misclassification-based weighted accuracy. The classification results on several high-dimensional biomedical datasets are compared.
机译：以前，我们引入了基于距离（相似性）的映射来可视化高维模式及其相对关系。映射在特殊的二维坐标系（相对距离平面（RDP））中精确地保留了从所有点到任意两个参考图案的原始距离。我们将RDP映射的适用性从可视化扩展到分类。几个分类器直接使用RDP。这些方法包括标准线性判别分析（LDA），最近邻分类器以及RDP中自然的基于变异概率的分类方法。也可以将多个参考方向组合起来以创建新的坐标系，在其中可以开发任意分类器。通过循环遍历所有可能的参考对并计算基于错误分类的加权准确性，我们获得了对分类结果更高的信心。比较了几个高维生物医学数据集的分类结果。

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