标题： RELARM：一种基于相对PCA属性和k均值聚类的评分模型 显示英文标题

标题： RELARM: A rating model based on relative PCA attributes and k-means clustering

摘要： 遵循在视觉识别中广泛使用的相对属性概念，文章为由其参数向量定义的一类对象建立了相对PCA属性的定义。 构建了一个新的评分模型（RELARM），该模型使用相对PCA属性排序函数对对象描述进行评分和k均值聚类算法。 每个评分对象到评分类别的评分分配是通过将聚类中心投影到特别选择的评分向量上得出的。 实证研究显示与现有的标准普尔、穆迪和惠誉评级具有很高的近似水平。 显示英文摘要

摘要： Following widely used in visual recognition concept of relative attributes, the article establishes definition of the relative PCA attributes for a class of objects defined by vectors of their parameters. A new rating model (RELARM) is built using relative PCA attribute ranking functions for rating object description and k-means clustering algorithm. Rating assignment of each rating object to a rating category is derived as a result of cluster centers projection on the specially selected rating vector. Empirical study has shown a high level of approximation to the existing S & P, Moody's and Fitch ratings.