如何在PCA中变白矩阵 [英] How to whiten matrix in PCA
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问题描述
我正在使用Python,并且已经使用本教程.
I'm working with Python and I've implemented the PCA using this tutorial.
一切正常,我进行了一次成功的协方差转换,将其转换为原始尺寸没问题.
Everything works great, I got the Covariance I did a successful transform, brought it make to the original dimensions not problem.
但是我该如何美白呢?我尝试将特征向量除以特征值:
But how do I perform whitening? I tried dividing the eigenvectors by the eigenvalues:
S, V = numpy.linalg.eig(cov)
V = V / S[:, numpy.newaxis]
,并使用V来转换数据,但这导致了奇怪的数据值. 有人可以切丝一点吗?
and used V to transform the data but this led to weird data values. Could someone please shred some light on this?
推荐答案
这是我从您还可以使用SVD增白矩阵:
You can also whiten a matrix using SVD:
def svd_whiten(X):
U, s, Vt = np.linalg.svd(X, full_matrices=False)
# U and Vt are the singular matrices, and s contains the singular values.
# Since the rows of both U and Vt are orthonormal vectors, then U * Vt
# will be white
X_white = np.dot(U, Vt)
return X_white
第二种方法稍慢一些,但数值上可能更稳定.
The second way is a bit slower, but probably more numerically stable.
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