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skmatter v0.4.dev29+ga1a316a

  • Getting started
  • Installation
  • API Reference
  • Examples
  • Contributing
    • Changelog
    • References
  • GitHub
  • Getting started
  • Installation
  • API Reference
  • Examples
  • Contributing
  • Changelog
  • References
  • GitHub

Section Navigation

  • PCovR and KernelPCovR
    • Choosing Different Regressors for PCovR
    • Construct a PCovR Map
    • The Importance of Data Scaling in PCovR / KernelPCovR
    • The Benefits of Kernel PCovR for the WHO Dataset
  • PCovC and KernelPCovC
    • Comparing PCovC with PCA and LDA
    • KPCovC Hyperparameter Tuning
    • PCovC Hyperparameter Tuning
    • Comparing KPCovC with KPCA
  • Feature and Sample Selection
    • Using scikit-matter selectors with scikit-learn pipelines
    • PCovR-Inspired Feature Selection
    • Generalized Convex Hull construction for the polymorphs of ROY
    • Feature Selection on the WHO Dataset
  • Regression
    • Regression with orthogonal projector/matrices
    • Ridge2FoldCV for data with low effective rank
  • Feature Reconstruction Measures
    • Pointwise Local Reconstruction Error
    • Global Feature Reconstruction Error (GFRE) and Distortion (GFRD)
    • Pointwise GFRE applied on RKHS features
  • Neighbors
    • Sparse KDE examples
    • Probabilistic Analysis of Molecular Motifs (PAMM)
  • Examples
  • PCovC and KernelPCovC

PCovC and KernelPCovC#

Comparing PCovC with PCA and LDA

Comparing PCovC with PCA and LDA

KPCovC Hyperparameter Tuning

KPCovC Hyperparameter Tuning

PCovC Hyperparameter Tuning

PCovC Hyperparameter Tuning

Comparing KPCovC with KPCA

Comparing KPCovC with KPCA

Download all examples in Python source code: pcovc_python.zip

Download all examples in Jupyter notebooks: pcovc_jupyter.zip

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© Copyright 2025, Rose K. Cersonsky, Guillaume Fraux, Sergei Kliavinek, Alexander Goscinski, Benjamin A. Helfrecht, Victor P. Principe, Philip Loche, Michele Ceriotti.

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