如何使用 scikit-learn 从线性判别分析中获取特征向量

如何从 scikit-learn 线性判别分析对象中获取变化基矩阵？

对于数组X有形状m x p (m样品和p特征）和N类，缩放矩阵有p行和N-1列。该矩阵可用于将数据从原始空间变换到线性子空间。

艾莉亚回答后编辑：

让我们考虑以下示例：

from sklearn.datasets import make_blobs
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA

X, label = make_blobs(n_samples=100, n_features=2, centers=5, cluster_std=0.10, random_state=0)
lda = LDA()
Xlda = lda.fit(X, label)
Xlda.scalings_
#array([[ 7.35157288,  6.76874473],
#       [-6.45391558,  7.97604449]])
Xlda.scalings_.shape
#(2, 2)

我希望 scalings_ 矩阵形状为 (2,4)，因为我有 2 个特征，并且 LDA 将提供 5-1 个分量。

让我们致电您的LinearDiscriminantAnalysis object lda。您可以访问缩放矩阵lda.scalings_。显示了描述这一点的文档here http://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.

import sklearn.datasets as ds
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA

iris = ds.load_iris()
iris.data.shape
# (150, 4)
len(iris.target_names)
# 3

lda = LDA()
lda.fit(iris.data, iris.target)
lda.scalings_
# array([[-0.81926852,  0.03285975],
#        [-1.5478732 ,  2.15471106],
#        [ 2.18494056, -0.93024679],
#        [ 2.85385002,  2.8060046 ]])
lda.scalings_.shape
# (4, 2)