Examples Gallery¶

The examples gallery provides working code samples demonstrating what can be done with the mvlearn library.

Examples on cluster¶

Multiview Coregularized Spectral Clustering Comparison — Multiview Coregularized Spectral Clustering Comparison¶

Multiview Spherical KMeans Tutorial — Multiview Spherical KMeans Tutorial¶

Multiview KMeans Tutorial — Multiview KMeans Tutorial¶

Multiview vs. Singleview Spectral Clustering of UCI Multiview Digits — Multiview vs. Singleview Spectral Clustering of UCI Multiview Digits¶

Multiview vs. Singleview KMeans — Multiview vs. Singleview KMeans¶

Multiview Spectral Clustering Tutorial — Multiview Spectral Clustering Tutorial¶

Multiview vs. Singleview Spectral Clustering — Multiview vs. Singleview Spectral Clustering¶

Conditional Independence of Views on Multiview Spectral Clustering — Conditional Independence of Views on Multiview Spectral Clustering¶

Conditional Independence of Views on Multiview KMeans Clustering — Conditional Independence of Views on Multiview KMeans Clustering¶

Examples on compose¶

Demonstrates usage of the methods and their integration with sklearn estimators and pipelines.

Constructing multiple views to classify singleview data — Constructing multiple views to classify singleview data¶

Integrating mvlearn with scikit-learn — Integrating mvlearn with scikit-learn¶

Examples on datasets¶

Loading and Viewing the UCI Multiple Features Dataset — Loading and Viewing the UCI Multiple Features Dataset¶

Generating Multiview Data from Gaussian Mixtures — Generating Multiview Data from Gaussian Mixtures¶

An mvlearn case study: the Nutrimouse dataset — An mvlearn case study: the Nutrimouse dataset¶

Examples on decomposition¶

ICA: a tutorial — ICA: a tutorial¶

Multiview Independent Component Analysis (ICA) Comparison — Multiview Independent Component Analysis (ICA) Comparison¶

Angle-based Joint and Individual Variation Explained (AJIVE) — Angle-based Joint and Individual Variation Explained (AJIVE)¶

Examples on embed¶

Generalized Canonical Correlation Analysis (GCCA) Tutorial — Generalized Canonical Correlation Analysis (GCCA) Tutorial¶

CCA Tutorial — CCA Tutorial¶

Deep CCA (DCCA) Tutorial — Deep CCA (DCCA) Tutorial¶

Partial Gram-Schmidt Orthogonalization (PGSO) for KMCCA — Partial Gram-Schmidt Orthogonalization (PGSO) for KMCCA¶

Multidimensional Scaling (MVMDS) Tutorial — Multidimensional Scaling (MVMDS) Tutorial¶

Comparing CCA Variants — Comparing CCA Variants¶

Kernel MCCA (KMCCA) Tutorial — Kernel MCCA (KMCCA) Tutorial¶

Examples on plotting¶

Quickly Visualizing Multiview Data — Quickly Visualizing Multiview Data¶

Plotting Multiview Data with a Cross-view Plot — Plotting Multiview Data with a Cross-view Plot¶

Examples on semi_supervised¶

2-View Semi-Supervised Regression — 2-View Semi-Supervised Regression¶

2-View Semi-Supervised Classification — 2-View Semi-Supervised Classification¶

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