July 2019
Intermediate to advanced
365 pages
8h 13m
Chinese
Content preview from Python机器学习手册：从数据预处理到深度学习
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170
｜
第
9
章
利用特征提取进行特征降维
矩阵分解为 ：
V
≈
WH
其中，
V
是
d
×
n
维特征矩阵（即
d
个特征，
n
个样本），
W
为
d
×
r
维矩阵，
H
是
r
×
n
维矩阵。
通过调整值，可以设定希望减少的维数。
如果要使用
NMA
，特征矩阵就不能包含负数值。此外，与
PCA
等前面探讨过的技术
不同，
NMA
不会告诉我们输出特征中保留了原始数据的信息量。因此，找出参数
n_
components
的最优值的最佳方法是不断尝试一系列可能的值，直到找出能生成最佳学习
模型的值（见第
12
章 ）。
延伸阅读
y
非负矩阵分解（
NMF
）（
http://bit.ly/2FvtWRj
）
9.5
对稀疏数据进行特征降维
问题描述
对稀疏特征矩阵进行特征降维操作。
解决方案
使用截断奇异值分解（
Truncated Singular Value Decomposition
，
TSVD
）法 ：
#
加载多个库
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import TruncatedSVD
from scipy.sparse import csr_matrix
from sklearn import datasets
import numpy as np
#
加载数据
digits = datasets.load_digits()
#
标准化特征矩阵
features = StandardScaler().fit_transform(digits.data) ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.