For two-class problems, we introduce and construct mappings of high-dimensional instances into dissimilarity (distance)-based Class-Proximity Planes. The Class Proximity Projections are extensions of our earlier relative distance plane mapping, and thus provide a more general and unified approach to the simultaneous classification and visualization of many-feature datasets. The mappings display all L-dimensional instances in two-dimensional coordinate systems, whose two axes represent the two distances of the instances to various pre-defined proximity measures of the two classes. The Class Proximity mappings provide a variety of different perspectives of the dataset to be classified and visualized. We report and compare the classification and visualization results obtained with various Class Proximity Projections and their combinations on four datasets from the UCI data base, as well as on a particular high-dimensional biomedical dataset.
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机译:对于两类问题,我们将高维实例的映射引入并构造到基于不相似(距离)的类邻近平面中。类邻近投影是我们之前的相对距离平面映射的扩展,因此为多特征数据集的同时分类和可视化提供了更通用和统一的方法。映射在二维坐标系中显示所有L维实例,其两个轴表示实例到两个类的各种预定义接近度的两个距离。 Class Proximity映射提供了要分类和可视化的数据集的各种不同角度。我们在UCI数据库的四个数据集以及特定的高维生物医学数据集上报告并比较了使用各种类别邻近投影及其组合获得的分类和可视化结果。
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