Partial least squares regression and projection on latent structure regression (PLS Regression)
Partial least squares regression and projection on latent structure regression (PLS Regression)
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DOI：
10.1002/wics.51
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发表时间：
2010-01-01
影响因子：
3.2
通讯作者：
Abdi, Herve
中科院分区：
文献类型：
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作者：
Abdi, Herve
偏最小二乘（PLS）回归（又称偏最小二乘回归）潜在结构上的投影）是一种最近的技术，它结合了主成分分析（PCA）和多元线性回归的特征。它的目标是从一组独立变量或预测变量预测一组因变量。这种预测是通过从预测因子中提取一组称为潜在变量的正交因子来实现的，这些因子具有最佳的预测能力。这些潜在变量可用于创建类似于PCA显示的显示。使用自助法和刀切法等交叉验证技术评估从偏最小二乘回归模型获得的预测质量。PLS回归有两种主要的变体：最常见的一种将因变量和自变量的角色分开;第二种主要用于分析大脑成像数据，对因变量和自变量赋予相同的角色。(C)John Wiley & Sons，Inc.
Partial least squares (PLS) regression (a.k.a. projection on latent structures) is a recent technique that combines features fromand generalizes principal component analysis (PCA) and multiple linear regression. Its goal is to predict a set of dependent variables from a set of independent variables or predictors. This prediction is achieved by extracting from the predictors a set of orthogonal factors called latent variables which have the best predictive power. These latent variables can be used to create displays akin to PCA displays. The quality of the prediction obtained from a PLS regression model is evaluated with cross-validation techniques such as the bootstrap and jackknife. There are two main variants of PLS regression: The most common one separates the roles of dependent and independent variables; the second one-used mostly to analyze brain imaging data-gives the same roles to dependent and independent variables. (C) 2010 John Wiley & Sons, Inc.