January 2021
Intermediate to advanced
384 pages
6h 40m
Chinese
Content preview from 基于Python的无监督学习
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116
｜
第
3
章
图
3
-
14
：用 t-SNE 分离观察值
字典学习
字典学习是另外一种方法。它学习原始数据的稀疏表示。生成的矩阵称为字典，
字典中的向量称为原子。这些原子是简单的二元向量，由
0
和
1
填充。原始
数据中的每个实例都可以重建为这些原子的加权和。
假设原始数据中有
d
特征，字典中有
m
个原子，我们就可以有一个字典，它
要么是在不完备，
n
<
d
，要么是过完备
,
n
>
d
。不完备字典实现了降维，用
较少的向量表示原始数据，这是我们将关注的重点。
注
3
有一个小批量版本的字典学习，我们将应用到我们的数据集。和其他降维方
法一样，我们将设置成分的数量，还将设置批量大小和迭代次数来执行训练。
由于想使用二维散点图来可视化图像，我们将学习一个非常密集的字典，但
练习中，我们将使用一个更稀疏的版本：
# Mini-batch dictionary learning
from sklearn.decomposition import MiniBatchDictionaryLearning
注
3
：
过完备字典有不同的用途，在图像压缩等方面有应用。
降维
｜
117
n_components = 50
alpha = 1
batch_size = 200
n_iter = 25
random_state = 2018
miniBatchDictLearning ...
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