D-CDLF：多视图高维数据的共同与独特潜在因子分解
D-CDLF: Decomposition of Common and Distinctive Latent Factors for Multi-view High-dimensional Data
AI总结：
本文提出D-CDLF分解方法，在双视图高维数据中同时实现共同潜在因子与独特潜在因子之间、以及不同视图独特潜在因子之间的不相关性，并讨论其高维估计。
摘要：
对多个高维数据视图进行联合分析的一种典型方法是将每个视图的数据矩阵分解为三个部分：由所有数据视图的共同潜在因子生成的低秩共同源矩阵、由相应数据视图的独特潜在因子生成的低秩独特源矩阵，以及加性噪声矩阵。现有的分解方法通常关注共同潜在因子与独特潜在因子之间的不相关性，但未能充分处理来自不同数据视图的独特潜在因子之间同样必要的不相关性。我们提出了一种新的分解方法，称为共同与独特潜在因子分解（D-CDLF），以有效实现双视图数据中这两种类型的不相关性。我们还讨论了D-CDLF在高维设置下的估计。
英文摘要：
A typical approach to the joint analysis of multiple high-dimensional data views is to decompose each view's data matrix into three parts: a low-rank common-source matrix generated by common latent factors of all data views, a low-rank distinctive-source matrix generated by distinctive latent factors of the corresponding data view, and an additive noise matrix. Existing decomposition methods often focus on the uncorrelatedness between the common latent factors and distinctive latent factors, but inadequately address the equally necessary uncorrelatedness between distinctive latent factors from different data views. We propose a novel decomposition method, called Decomposition of Common and Distinctive Latent Factors (D-CDLF), to effectively achieve both types of uncorrelatedness for two-view data. We also discuss the estimation of the D-CDLF under high-dimensional settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
