class sklearn.ensemble.RandomTreesEmbedding(n_estimators=10, max_depth=5, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0

sklearn.decomposition.sparse_encode(X, dictionary, gram=None, cov=None, algorithm='lasso_lars', n_nonzero_coefs=None, alpha=None

class sklearn.decomposition.MiniBatchDictionaryLearning(n_components=None, alpha=1, n_iter=1000, fit_algorithm='lars'

We want to compare the performance of the MiniBatchKMeans and KMeans: the MiniBatchKMeans is faster, but gives slightly different

Warning DEPRECATED

class sklearn.preprocessing.Normalizer(norm='l2', copy=True)

class sklearn.svm.LinearSVC(penalty='l2', loss='squared_hinge', dual=True, tol=0.0001, C=1.0, multi_class='ovr', fit_intercept=True, intercept_scaling=1

class sklearn.preprocessing.RobustScaler(with_centering=True, with_scaling=True, quantile_range=(25.0, 75.0), copy=True)

Face, a 1024 x 768 size image of a raccoon face, is used here to illustrate how k-means is used for vector quantization.

class sklearn.svm.SVC(C=1.0, kernel='rbf', degree=3, gamma='auto', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None