2025年2月10日 · Linear Discriminant Analysis (LDA) also known as Normal Discriminant Analysis is supervised classification problem that helps separate two or more classes by converting higher-dimensional data space into a lower-dimensional space. It is used to identify a linear combination of features that best separates classes within a dataset.

Linear discriminant analysis (LDA), normal discriminant analysis (NDA), canonical variates analysis (CVA), or discriminant function analysis is a generalization of Fisher's linear discriminant, a method used in statistics and other fields, to find a linear combination of features that characterizes or separates two or more classes of objects or ...

线性判别分析 (LDA) 是一种在监督机器学习中用于解决多类分类问题的方法。 LDA 可通过数据降维对具有多个特征的多个类进行分离。 此技术在数据科学中非常重要，因为它有助于优化机器学习模型。 线性判别分析也称为正态判别分析 (NDA) 或判别函数分析 (DFA)，它采用生成式模型框架。 这意味着 LDA 算法对每个类别的数据分布进行建模，并使用 贝叶斯定理1 对新数据点进行分类。 贝叶斯定理计算条件概率，表示在某个事件发生的情况下另一个事件发生的概率。 LDA 算 …

Linear Discriminant Analysis (LDA) is one of the commonly used dimensionality reduction techniques in machine learning to solve more than two-class classification problems. It is also known as Normal Discriminant Analysis (NDA) or Discriminant Function Analysis (DFA).

2021年11月9日 · Linear Discriminant Analysis (LDA) is a dimensionality reduction technique commonly used for supervised classification problems. The goal of LDA is to project the dataset onto a lower-dimensional space while maximizing the class separability. LDA is very similar to Principal Component Analysis (PCA), but there are some important differences.

本文深入讲解了PCA、SVD、LDA、MDS、Isomap、LLE和LE等多种降维技术的原理与应用，包括无监督和监督学习的降维方法，以及流形学习的几种典型算法。 ML-降维：PCA、SVD、LDA、MDS、LLE、LE算法总结

费舍尔线性判别分析（Fisher's Linear Discriminant Analysis，简称FLDA或LDA）是一种统计学方法，常用于高维数据的降维和分类。由英国统计学家Ronald Aylmer Fisher在1936年提出，它旨在找到一个线性的投影空间，...

2019年3月4日 · lda是一种监督学习的降维技术，也就是说它的数据集的每个样本是有类别输出的。 这点和PCA不同。 核心思想是投影后类内方差最小，类间方差最大，如下右图（2维到1维），显然比左图更符合这个思想，LDA就是希望降维后的数据，能最大化的满足这个。

2023年8月23日 · “ Linear Discriminant Analysis (LDA) is a dimensionality reduction and classification technique commonly used in machine learning and pattern recognition. In the...

2020年8月15日 · In this post you will discover the Linear Discriminant Analysis (LDA) algorithm for classification predictive modeling problems. After reading this post you will know: The limitations of logistic regression and the need for linear discriminant analysis. The representation of the model that is learned from data and can be saved to file.