Class KernelPCAScikitsLearnNode

Kernel Principal component analysis (KPCA)

This node has been automatically generated by wrapping the ``sklearn.decomposition.kernel_pca.KernelPCA`` class
from the ``sklearn`` library.  The wrapped instance can be accessed
through the ``scikits_alg`` attribute.

Non-linear dimensionality reduction through the use of kernels (see
:ref:`metrics`).

Read more in the :ref:`User Guide <kernel_PCA>`.

**Parameters**

n_components: int or None
    Number of components. If None, all non-zero components are kept.

kernel: "linear" | "poly" | "rbf" | "sigmoid" | "cosine" | "precomputed"
    Kernel.
    Default: "linear"

degree : int, default=3
    Degree for poly kernels. Ignored by other kernels.

gamma : float, optional
    Kernel coefficient for rbf and poly kernels. Default: 1/n_features.
    Ignored by other kernels.

coef0 : float, optional
    Independent term in poly and sigmoid kernels.
    Ignored by other kernels.

kernel_params : mapping of string to any, optional
    Parameters (keyword arguments) and values for kernel passed as
    callable object. Ignored by other kernels.

alpha: int
    Hyperparameter of the ridge regression that learns the
    inverse transform (when fit_inverse_transform=True).
    Default: 1.0

fit_inverse_transform: bool
    Learn the inverse transform for non-precomputed kernels.
    (i.e. learn to find the pre-image of a point)
    Default: False

eigen_solver: string ['auto'|'dense'|'arpack']
    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
    convergence tolerance for arpack.
    Default: 0 (optimal value will be chosen by arpack)

max_iter : int
    maximum number of iterations for arpack
    Default: None (optimal value will be chosen by arpack)

remove_zero_eig : boolean, default=True
    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.

**Attributes**


``lambdas_`` :

    - Eigenvalues of the centered kernel matrix


``alphas_`` :

    - Eigenvectors of the centered kernel matrix


``dual_coef_`` :

    - Inverse transform matrix


``X_transformed_fit_`` :

    - Projection of the fitted data on the kernel principal components


**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.

Overrides: object.__init__

Transform X.

This node has been automatically generated by wrapping the sklearn.decomposition.kernel_pca.KernelPCA class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

X: array-like, shape (n_samples, n_features)

Returns

X_new: array-like, shape (n_samples, n_components)

Overrides: Node.execute

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Fit the model from data in X.

This node has been automatically generated by wrapping the sklearn.decomposition.kernel_pca.KernelPCA class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

X: array-like, shape (n_samples, n_features)

Training vector, where n_samples in the number of samples and n_features is the number of features.

Returns

self : object

Returns the instance itself.

Overrides: Node.stop_training