论文标题
广义特征，奇异值和部分最小二乘分解：GSVD包装
Generalized eigen, singular value, and partial least squares decompositions: The GSVD package
论文作者
论文摘要
广义奇异值分解（GSVD，又称“ SVD三重态”，“二元图”方法）提供了统一的策略和基础，以执行几乎所有最常见的多元分析（例如，主要成分，对应分析，多维尺度尺度，规范级别率，规范相关性，部分最小分）。尽管GSVD无处不在，强大且灵活，但其实现很少。在这里，我介绍了GSVD的GSVD软件包。GSVD的一般目标是提供一系列可访问的功能来执行GSVD和其他两个相关的分解（广义特征值分解，广义部分最小二乘平方s-Sningular值分解）。此外，GSVD有助于为许多技术提供更统一的概念方法和命名法。我首先介绍了GSVD的概念，然后是对广义分解的形式定义。接下来，我提供了一些在开发过程中做出的关键决定，然后提供了一些有关如何使用GSVD实施各种统计技术的示例。这些示例还说明了GSVD的目标之一：其他人如何（或应该）构建依赖GSVD的分析软件包。最后，我讨论了GSVD可能的未来。
The generalized singular value decomposition (GSVD, a.k.a. "SVD triplet", "duality diagram" approach) provides a unified strategy and basis to perform nearly all of the most common multivariate analyses (e.g., principal components, correspondence analysis, multidimensional scaling, canonical correlation, partial least squares). Though the GSVD is ubiquitous, powerful, and flexible, it has very few implementations. Here I introduce the GSVD package for R. The general goal of GSVD is to provide a small set of accessible functions to perform the GSVD and two other related decompositions (generalized eigenvalue decomposition, generalized partial least squares-singular value decomposition). Furthermore, GSVD helps provide a more unified conceptual approach and nomenclature to many techniques. I first introduce the concept of the GSVD, followed by a formal definition of the generalized decompositions. Next I provide some key decisions made during development, and then a number of examples of how to use GSVD to implement various statistical techniques. These examples also illustrate one of the goals of GSVD: how others can (or should) build analysis packages that depend on GSVD. Finally, I discuss the possible future of GSVD.
