sklearn.decomposition.KernelPCA — scikit-learn 0.20.4 documentation (original) (raw)

Kernel Principal component analysis (KPCA)

Non-linear dimensionality reduction through the use of kernels (seePairwise metrics, Affinities and Kernels).

Read more in the User Guide.

| Parameters: | n_components : int, default=None Number of components. If None, all non-zero components are kept. kernel : “linear” | “poly” | “rbf” | “sigmoid” | “cosine” | “precomputed” Kernel. Default=”linear”. gamma : float, default=1/n_features Kernel coefficient for rbf, poly and sigmoid kernels. Ignored by other kernels. degree : int, default=3 Degree for poly kernels. Ignored by other kernels. coef0 : float, default=1 Independent term in poly and sigmoid kernels. Ignored by other kernels. kernel_params : mapping of string to any, default=None Parameters (keyword arguments) and values for kernel passed as callable object. Ignored by other kernels. alpha : int, default=1.0 Hyperparameter of the ridge regression that learns the inverse transform (when fit_inverse_transform=True). fit_inverse_transform : bool, default=False Learn the inverse transform for non-precomputed kernels. (i.e. learn to find the pre-image of a point) eigen_solver : string [‘auto’|’dense’|’arpack’], default=’auto’ Select eigensolver to use. If n_components is much less than the number of training samples, arpack may be more efficient than the dense eigensolver. tol : float, default=0 Convergence tolerance for arpack. If 0, optimal value will be chosen by arpack. max_iter : int, default=None Maximum number of iterations for arpack. If None, optimal value will be chosen by arpack. remove_zero_eig : boolean, default=False If True, then all components with zero eigenvalues are removed, so that the number of components in the output may be < n_components (and sometimes even zero due to numerical instability). When n_components is None, this parameter is ignored and components with zero eigenvalues are removed regardless. random_state : int, RandomState instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random. Used when eigen_solver == ‘arpack’. New in version 0.18. copy_X : boolean, default=True If True, input X is copied and stored by the model in the X_fit_attribute. If no further changes will be done to X, settingcopy_X=False saves memory by storing a reference. New in version 0.18. n_jobs : int or None, optional (default=None) The number of parallel jobs to run.None means 1 unless in a joblib.parallel_backend context.-1 means using all processors. See Glossaryfor more details. New in version 0.18. | | ----------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----- | --------- | -------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Attributes: | lambdas_ : array, (n_components,) Eigenvalues of the centered kernel matrix in decreasing order. If n_components and remove_zero_eig are not set, then all values are stored. alphas_ : array, (n_samples, n_components) Eigenvectors of the centered kernel matrix. If n_components andremove_zero_eig are not set, then all components are stored. dual_coef_ : array, (n_samples, n_features) Inverse transform matrix. Only available whenfit_inverse_transform is True. X_transformed_fit_ : array, (n_samples, n_components) Projection of the fitted data on the kernel principal components. Only available when fit_inverse_transform is True. X_fit_ : (n_samples, n_features) The data used to fit the model. If copy_X=False, then X_fit_ is a reference. This attribute is used for the calls to transform. | | | | |

References

Kernel PCA was introduced in:

Bernhard Schoelkopf, Alexander J. Smola, and Klaus-Robert Mueller. 1999. Kernel principal component analysis. In Advances in kernel methods, MIT Press, Cambridge, MA, USA 327-352.

Examples

from sklearn.datasets import load_digits from sklearn.decomposition import KernelPCA X, _ = load_digits(return_X_y=True) transformer = KernelPCA(n_components=7, kernel='linear') X_transformed = transformer.fit_transform(X) X_transformed.shape (1797, 7)

Methods

__init__(n_components=None, kernel='linear', gamma=None, degree=3, coef0=1, kernel_params=None, alpha=1.0, fit_inverse_transform=False, eigen_solver='auto', tol=0, max_iter=None, remove_zero_eig=False, random_state=None, copy_X=True, n_jobs=None)[source]¶

fit(X, y=None)[source]¶

Fit the model from data in X.

fit_transform(X, y=None, **params)[source]¶

Fit the model from data in X and transform X.

get_params(deep=True)[source]¶

Get parameters for this estimator.

inverse_transform(X)[source]¶

Transform X back to original space.

References

“Learning to Find Pre-Images”, G BakIr et al, 2004.

set_params(**params)[source]¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form<component>__<parameter> so that it’s possible to update each component of a nested object.

transform(X)[source]¶

Transform X.