NMF（非负矩阵分解）人脸数据特征提取
import matplotlib.pyplot as plt
from sklearn import decomposition
from sklearn.datasets import fetch_olivetti_faces
from numpy.random import RandomState
n_row, n_col = 2, 3
n_components = n_row * n_col
image_shape = (64, 64)
dataset = fetch_olivetti_faces(shuffle=True, random_state= RandomState(0))
faces = dataset.data
def plot_gallery(title, images, n_col = n_col, n_row = n_row):
plt.figure(figsize=(2.* n_col, 2.26 * n_row))
plt.suptitle(title, size=16)
for i, comp in enumerate(images):
plt.subplot(n_row, n_col, i + 1)
vmax = max(comp.max(), -comp.min())
plt.imshow(comp.reshape(image_shape), cmap=plt.cm.gray, interpolation='nearest', vmin = -vmax, vmax = vmax)
plt.xticks(())
plt.yticks(())
plt.subplots_adjust(0.01, 0.05, 0.99, 0.93, 0.04, 0.)
estimators = [
('Eigenfaces - PCA using randomized SVD', decomposition.PCA(n_components=6, whiten=True)),
('Non-negative components -NMF', decomposition.NMF(n_components=6, init='nndsvda', tol=5e-3))
]
for name, estimators in estimators:
estimators.fit(faces)
components_ = estimators.components_
plot_gallery(name, components_[:n_components])
plt.show()
作者：Drchen_usts
