CDPA: Common and Distinctive Pattern Analysis between High-dimensional Datasets.

IF 1 4区 数学 Q3 STATISTICS & PROBABILITY
Electronic Journal of Statistics Pub Date : 2022-01-01 Epub Date: 2022-04-04 DOI:10.1214/22-EJS2008
Hai Shu, Zhe Qu
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引用次数: 1

Abstract

A representative model in integrative analysis of two high-dimensional correlated datasets is to decompose each data matrix into a low-rank common matrix generated by latent factors shared across datasets, a low-rank distinctive matrix corresponding to each dataset, and an additive noise matrix. Existing decomposition methods claim that their common matrices capture the common pattern of the two datasets. However, their so-called common pattern only denotes the common latent factors but ignores the common pattern between the two coefficient matrices of these common latent factors. We propose a new unsupervised learning method, called the common and distinctive pattern analysis (CDPA), which appropriately defines the two types of data patterns by further incorporating the common and distinctive patterns of the coefficient matrices. A consistent estimation approach is developed for high-dimensional settings, and shows reasonably good finite-sample performance in simulations. Our simulation studies and real data analysis corroborate that the proposed CDPA can provide better characterization of common and distinctive patterns and thereby benefit data mining.

CDPA:高维数据集之间的共同和独特模式分析。
两个高维相关数据集集成分析的代表性模型是将每个数据矩阵分解为数据集间共享的潜在因素生成的低秩公共矩阵、每个数据集对应的低秩特征矩阵和加性噪声矩阵。现有的分解方法声称它们的公共矩阵捕获两个数据集的公共模式。然而,它们的所谓共同模式只表示共同潜在因素,而忽略了这些共同潜在因素的两个系数矩阵之间的共同模式。我们提出了一种新的无监督学习方法,称为共同和独特模式分析(CDPA),它通过进一步结合系数矩阵的共同和独特模式来适当地定义两种类型的数据模式。提出了一种适用于高维环境的一致性估计方法,并在仿真中显示出较好的有限样本性能。我们的模拟研究和实际数据分析证实了所提出的CDPA可以更好地表征常见和独特的模式,从而有利于数据挖掘。
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来源期刊
Electronic Journal of Statistics
Electronic Journal of Statistics STATISTICS & PROBABILITY-
CiteScore
1.80
自引率
9.10%
发文量
100
审稿时长
3 months
期刊介绍: The Electronic Journal of Statistics (EJS) publishes research articles and short notes on theoretical, computational and applied statistics. The journal is open access. Articles are refereed and are held to the same standard as articles in other IMS journals. Articles become publicly available shortly after they are accepted.
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