Kernel Principal Component Analysis (KPCA)

Some of the docstrings for this module have been automatically extracted from the scikit-learn library and are covered by their respective licenses.

class node_decomposition.KernelPCA

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”.

• 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).

• fit_inverse_transform

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

• 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

  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.

• n_jobs

  The number of parallel jobs to run. If -1, then the number of jobs is set to the number of CPU cores.

  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. Set if fit_inverse_transform is True.

• X_transformed_fit_

  Projection of the fitted data on the kernel principal components.

• 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.

Inputs: Outputs:

model : model

Model