October 2020
Intermediate to advanced
693 pages
16h 26m
Chinese
Content preview from 机器学习实战：基于Scikit-Learn、Keras 和TensorFlow （原书第2 版）
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206
｜
第
8
章
原始空间
重构原像
原像误差
降维的空间
特征空间
重建
图 8-11：内核 PCA 和重构原像误差
rbf_pca = KernelPCA(n_components = 2, kernel="rbf", gamma=0.0433,
fit_inverse_transform=True)
X_reduced = rbf_pca.fit_transform(X)
X_preimage = rbf_pca.inverse_transform(X_reduced)
默认情况下，fit_inverse_transform = False，并且 KernelPCA 没有
inverse_transform() 方法。仅当你设置 fit_inverse_transform =
True 时，才会创建此方法。
计算重建原像误差：
>>> from sklearn.metrics import
mean_squared_error
>>>
mean_squared_error(X, X_preimage)
32.786308795766132
现在，你可以使用网格搜索与交叉验证来找到可最大限度减少此错误的内核和超参数。
1
8.5 LLE
局部线性嵌入（ LLE）是另一种强大的非线性降维（NLDR）技术
注 8
。它是一种流形学习
技术，不像以前的算法那样依赖于投影。简而言之，LLE 的工作原理是首先测量每个训
练实例如何与其最近的邻居（c.n.）线性相关，然后寻找可以最好地保留这些局部关系的
注 8 ：Sam T. Roweis ...
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