标题： 从混合的真实和分类关系数据推断潜在结构 显示英文标题

标题： Inferring Latent Structure From Mixed Real and Categorical Relational Data

摘要： 我们考虑对关系数据（矩阵）的分析，其中矩阵的行对应于主体（例如，人），列对应于属性。 矩阵的元素可能是实值和类别值的混合。 每个主体和属性由一个潜在的二进制特征向量表征，推断出的矩阵将每一对二进制特征向量的行-列映射到观察到的矩阵元素。 行的潜在二进制特征通过具有低秩协方差矩阵的多元高斯分布建模，并且高斯随机变量通过probit链接映射到潜在的二进制特征。 相同类型的构造被联合应用于列。 该模型推断与每一行和每一列相关的潜在、低维二进制特征，并推断所有行之间以及所有列之间的相关结构。 显示英文摘要

摘要： We consider analysis of relational data (a matrix), in which the rows correspond to subjects (e.g., people) and the columns correspond to attributes. The elements of the matrix may be a mix of real and categorical. Each subject and attribute is characterized by a latent binary feature vector, and an inferred matrix maps each row-column pair of binary feature vectors to an observed matrix element. The latent binary features of the rows are modeled via a multivariate Gaussian distribution with low-rank covariance matrix, and the Gaussian random variables are mapped to latent binary features via a probit link. The same type construction is applied jointly to the columns. The model infers latent, low-dimensional binary features associated with each row and each column, as well correlation structure between all rows and between all columns.