Question

是否有任何可用的示例可以在数据集上提供主成分分析的动手示例？我正在阅读仅讨论理论的文章，我正在寻找能告诉我如何使用PCA然后解释结果并将原始数据集转换为新数据集的内容。有什么建议吗？

Answer 1

如果您了解Python，这里有一个简短的动手示例：

# Generate correlated data from uncorrelated data.
# Each column of X is a 3-dimensional feature vector.
Z = scipy.randn(3, 1000)
C = scipy.randn(3, 3)
X = scipy.dot(C, Z)

# Visualize the correlation among the features.
pylab.scatter(X[0,:], X[1,:])
pylab.scatter(X[0,:], X[2,:])
pylab.scatter(X[1,:], X[2,:])

# Perform PCA. It can be shown that the principal components of the 
# matrix X are equivalent to the left singular vectors of X, which are
# equivalent to the eigenvectors of X X^T (up to indeterminacy in sign).
U, S, Vh = scipy.linalg.svd(X)
W, Q = scipy.linalg.eig(scipy.dot(X, X.T))
print U
print Q

# Project the original features onto the eigenspace.
Y = scipy.dot(U.T, X)

# Visualize the absence of correlation among the projected features.
pylab.scatter(Y[0,:], Y[1,:])
pylab.scatter(Y[1,:], Y[2,:])
pylab.scatter(Y[0,:], Y[2,:])

Answer 2

你可以检查http://alias-i.com/lingpipe/demos/tutorial/svd/read-me.html SVD和LSA非常相似的PCA方法都是空间缩减方法。基础评估方法的唯一区别。

Answer 3

由于您要求提供实用的动手示例，here您可以使用互动演示。

主成分分析的工作实例？

3 个答案: