机器学习-特征抽取-LDA(Linear Discriminant Analysis)

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最新推荐文章于 2025-04-24 16:06:32 发布 · 1k 阅读

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Section I: Brief Introduction on LDA

Linear Discriminat Analysis (LDA) can be used as a technique for feature extraction to increase the computational efficiency and reduce the degree of overfitting due to the curse of dimensionality in non-regularized model. The general concept behind LDA is very similar to PCA. Whereas PCA attempts to find the orthogonal component axes of maximum variance in a dataset, the goal in LDA is to find the feature subspace that optimizes class separability. In contrast with PCA, LDA is a supervised algorithm.

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LDA是一种监督学习算法，其在于利用类别标签来计算类间距离和类内部距离矩阵后，以类似于PCA算法计算两者矩阵整体的特征值和特征向量。因此，从某种角度，LDA和PCA是略微相似的。

FROM
Sebastian Raschka, Vahid Mirjalili. Python机器学习第二版. 南京：东南大学出版社，2018.

Section II: Code Bundle

代码：

import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
import numpy as np
from sklearn