Kernel Principal Component Analysis (KPCA)

../../../../_images/PCA.svg

Non-linear dimensionality reduction through the use of kernels

Documentation

Non-linear dimensionality reduction through the use of kernels

Configuration:

  • n_components

    Number of components. If None, all non-zero components are kept.

  • kernel

    Kernel. Default=”linear”.

  • fit_inverse_transform

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

  • degree

    Degree for poly kernels. Ignored by other kernels.

  • gamma

    Kernel coefficient for rbf, poly and sigmoid kernels. Ignored by other kernels.

  • coef0

    Independent term in poly and sigmoid kernels. Ignored by other kernels.

  • alpha

    Hyperparameter of the ridge regression that learns the inverse transform (when fit_inverse_transform=True).

  • remove_zero_eig

    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.

  • eigen_solver

    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

    Convergence tolerance for arpack. If 0, optimal value will be chosen by arpack.

  • max_iter

    Maximum number of iterations for arpack. If None, optimal value will be chosen by arpack.

  • random_state

    Used when eigen_solver == ‘arpack’. Pass an int for reproducible results across multiple function calls. See random_state.

    New in version 0.18.

  • n_jobs

    The number of parallel jobs to run. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See n_jobs for more details.

    New in version 0.18.

Attributes:

  • lambdas_

    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_

    Eigenvectors of the centered kernel matrix. If n_components and remove_zero_eig are not set, then all components are stored.

  • dual_coef_

    Inverse transform matrix. Only available when fit_inverse_transform is True.

  • X_transformed_fit_

    Projection of the fitted data on the kernel principal components. Only available when fit_inverse_transform is True.

  • X_fit_

    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.

Input ports:

Output ports:
modelmodel

Model

Definition

Input ports

Output ports

model

model

Model

class node_decomposition.KernelPCA[source]