论文标题
使用Beta分布的信息几何形状对医疗数据进行分类
Classifying histograms of medical data using information geometry of beta distributions
论文作者
论文摘要
在本文中，我们使用信息几何图形工具来比较，平均和分类直方图。 Beta分布拟合到直方图，并使用相应的Fisher信息几何形状进行比较。我们表明，这种几何形状是负弯曲的，可以保证均值概念的唯一性，并使其适合通过流行的K-均值算法对直方图进行分类。我们说明了这些几何工具在两种医学数据集的监督和无监督分类程序中的使用，心形变形以检测肺动脉高压和脑皮质厚度，以诊断阿尔茨海默氏病。
In this paper, we use tools of information geometry to compare, average and classify histograms. Beta distributions are fitted to the histograms and the corresponding Fisher information geometry is used for comparison. We show that this geometry is negatively curved, which guarantees uniqueness of the notion of mean, and makes it suitable to classify histograms through the popular K-means algorithm. We illustrate the use of these geometric tools in supervised and unsupervised classification procedures of two medical data-sets, cardiac shape deformations for the detection of pulmonary hypertension and brain cortical thickness for the diagnosis of Alzheimer's disease.
