机器学习-特征抽取-主成分分析法(Principal Component Analysis)

PCA在机器学习中的应用：特征抽取与降维

最新推荐文章于 2025-12-03 18:08:08 发布

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最新推荐文章于 2025-12-03 18:08:08 发布 · 794 阅读

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PCA是一种用于高维数据降维的方法，寻找最大方差方向并创建新的正交特征。通过PCA，可以从13维特征降至2维，仍能有效区分数据类别，如酒的种类。该文结合Sebastian Raschka和Vahid Mirjalili的《Python机器学习》进行介绍。

Section I: Brief Introduction on PCA

PCA helps us to identify patterns in data based on the correlation between features. In a nutshell, PCA aims to find the directions of maximum variance in high-dimensional data and projects it onto a new subspace with equal or fewer dimensions than the original one. The orthogonal axes (principal components) of new subspace can be interpreted as the directions of maximum variance given the constaint that the new feature aes are orthogonal to each other.
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 Boundle

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.linear_model import LogisticRegression
from sklearn.decomposition import PCA
from PCA.visualize import plot_decision_regions

#Section 1: Prepare data
plt.rcParams['figure.dpi']=200