2023年7月14日 · 正交偏最小二乘判别分析 （Orthogonal PLS-DA，OPLS-DA ）是一种有监督的判别分析方法，是多变量统计分析方法。OPLS-DA综合了PLS-DA和 正交信号 过滤（orthogonal signal correction, OSC）技术，能够把与预先设定的和分类无关的信息最大程度从原始矩阵分 …

2024年3月18日 · PLS-DA(Partial Least Squares Discriminant Analysis)，即偏最小二乘法判别分析，是多变量数据分析技术中的判别分析法，经常用来处理分类和判别问题。通过对主成分适当的旋转，PLS-DA可以有效的对组间观察值进行区分，并且能够找到导致组间区别的影响变量。

PLS-DA(Partial Least Squares Discriminant Analysis)，即偏最小二乘法判别分析，是多变量数据分析技术中的判别分析法，经常用来处理分类和判别问题。 通过对主成分适当的旋转，PLS-DA可以有效的对组间观察值进行区分，并且能够找到导致组间区别的影响变量。

PLS-DA is a supervised dimensionality reduction method that, unlike PCA, incorporates class labels in the analysis. It discovers latent variables (components) that maximize the separation between different predefined groups (e.g., healthy vs diseased) in the data.

2020年6月1日 · 偏最小二乘法判别分析（PLS-DA,Partial Least Squares Discriminant Analysis）经常用来处理分类和判别问题。其与PCA类似，不同的是PCA是无监督的，PLS-DA是有监督的。 当样本组间差异大而组内差异小时，无监督分析方法可以很好的区分组间差异。

“Multiclass partial least squares discriminant analysis: Taking the right way—A critical tutorial,” by Pomerantsev and Rodionova, Journal of Chemometrics, 32 (2018) suggests 2 approaches to PLS-DA. These are “hard” and “soft” PLS-DA, which are distinguished by how they determine their discrimination boundaries. Both begin in the ...

2020年12月9日 · Partial Least-Squares Discriminant Analysis (PLS-DA) is a popular machine learning tool that is gaining increasing attention as a useful feature selector and classifier. In an effort to understand its strengths and weaknesses, we performed a series of experiments with synthetic data and compared its performance to its close relative from which ...

Performs Partial Least Squares - Discriminant Analysis on two datasets. Integrate two datasets with PLS-DA, a supervised approach to project individuals described by numeric variables (in a first dataset, X) on a subspace where they are separated at best based on their value for a categorical variable (in a second dataset, Y).

2018年6月1日 · Partial least squares-discriminant analysis (PLS-DA) is a versatile algorithm that can be used for predictive and descriptive modelling as well as for discriminative variable selection. However, versatility is both a blessing and a curse and the user needs to optimize a wealth of parameters before reaching r Recent Review Articles

The purpose of this document is to describe a step-by-step Partial Least Squares Discriminant Analysis (PLS-DA) workflow. For illustration purpose, we upload one numerical datasets proteins and a general dataset with categorical (one will be the target variable of the PLS-DA), clinical.