NMF
NMF# class sklearn.decomposition.NMF(n_components='auto', *, init=None, solver='cd', beta_loss='frobenius', tol=0.0001, max_iter=200, random_state=None, alpha_W=0.0, alpha_H='same', l1_ratio=0.0, verbose=0, shuffle=False)[source]# Non-Negative Matrix Factorization (NMF). Find two non-negative matrices, i.e. matrices with all non-negative elements, (W, H) whose product approximates the non-negative
sklearn.decomposition.RandomizedPCA — scikit-learn 0.19.2 documentation
class sklearn.decomposition.RandomizedPCA(*args, **kwargs)[source]¶ Principal component analysis (PCA) using randomized SVD Deprecated since version 0.18: This class will be removed in 0.20. Use PCA with parameter svd_solver ‘randomized’ instead. The new implementation DOES NOT store whiten components_. Apply transform to get them. Linear dimensionality reduction using approximated Singular Value
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