from sklearn.datasets import load_digits
digits = load_digits()
import matplotlib.pyplot as plt
%matplotlib inline
from sklearn.decomposition import PCA
pca = PCA(n_components=2)
pca.fit(digits.data)
digits_pca = pca.transform(digits.data)
digits_pca.shape
plt.scatter(digits_pca[:, 0], digits_pca[:, 1], c=digits.target)
plt.matshow(pca.mean_.reshape(8, 8))
plt.matshow(pca.components_[0].reshape(8, 8))
plt.matshow(pca.components_[1].reshape(8, 8))
from sklearn.manifold import SpectralEmbedding
se = SpectralEmbedding()
digits_se = se.fit_transform(digits.data)
plt.scatter(digits_se[:, 0], digits_se[:, 1], c=digits.target)
n_neighbors
parameter of Spectral Embedding. How doe that change the outcome?