2019年2月22日 · 《LDA数学八卦》大概于2013年发表在我爱自然语言处理博客上，是我的前腾讯Leader rickjin的作品，应该也是目前中文互联网上介绍LDA最好的材料之一；该系列大概在2015年被搬运到公众号上，不过那个时候关注AINLP公众号的同学还不多，所以这里做个索引： LDA数学 ...

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

LDA: multivariate normal with equal covariance# LDA is the special case of the above strategy when \(P(X \mid Y=k) = N(\mu_k, \mathbf\Sigma)\) . That is, within each class the features have multivariate normal distribution with center depending on the class and common covariance \(\mathbf\Sigma\) .

2025年1月1日 · 线性判别式分析（Linear Discriminant Analysis, LDA）是一种经典的统计方法，常用于特征降维和分类问题。在机器学习领域，LDA通过最大化类间距离与类内距离的比值，来寻找能够最好区分不同类别的新特征空间。MATLAB...

lda的思想可以用一句话概括，就是“投影后类内方差最小，类间方差最大”。 什么意思呢？ 我们要将数据在低维度上进行投影，投影后希望每一种类别数据的投影点尽可能的接近，而不同类别的数据的类别中心之间的距离尽可能的大。

线性判别分析，英文名称Linear Discriminant Analysis (LDA)是一种经典的线性学习方法。 本文针对二分类问题，从直观理解，对其数学建模，之后模型求解，再拓展到多分类问题。 给定训练样例集，设法将样例投影到一条直线上，使得 同类样例的投影点尽可能接近 、 异类样例的投影点尽可能远离；在对新样本进行分类时，将其投影到同样的这条直线上，再根据投影点的位置来确定新样本的类别。 道理是这么个道理，我们现在需要在数学上对其进行分析。 我们接下来先建立求 …

LinearDiscriminantAnalysis(LDA) Datarepresentationvsdataclassification PCA aims to find the most accurate data representation in a lower dimen-

2025年2月28日 · In this section, we will present a numerical example explaining how to calculate the LDA space step by step and how LDA is used to discriminate two different classes of data using the class-independent and class-dependent approach.

(sum of square differences between the projected samples and their class mean). measures the variability within class ωi after projecting it on the y-space. two classes at hand after projection, hence it is called within-class scatter of the projected samples.