Example gallery

We provide here different examples on how to use the GemClus library, from clustering to variable selection.

General examples

An introducing example to clustering with an MLP and the MMD GEMINI

An introducing example to clustering with an MLP and the MMD GEMINI

Non parametric clustering

Non parametric clustering

Kernel KMeans clustering with GEMINI

Kernel KMeans clustering with GEMINI

Example of decision boundary map for a mixture of Gaussian and low-degree Student distributions

Example of decision boundary map for a mixture of Gaussian and low-degree Student distributions

Clustering circles with kernel RIM

Clustering circles with kernel RIM

Clustering with the squared-loss mutual information

Clustering with the squared-loss mutual information

Drawing a decision boundary between two interlacing moons

Drawing a decision boundary between two interlacing moons

Simple logistic regression with RIM

Simple logistic regression with RIM

Graph node clustering with a nonparametric model

Graph node clustering with a nonparametric model

Comparative clustering of circles dataset with kernel change

Comparative clustering of circles dataset with kernel change

Extending GemClus to build your own discriminative clustering model

Extending GemClus to build your own discriminative clustering model

Feature selection

Feature selection using the Sparse MMD OvO (Logistic regression)

Feature selection using the Sparse MMD OvO (Logistic regression)

Feature selection using the Sparse Linear MI (Logistic regression)

Feature selection using the Sparse Linear MI (Logistic regression)

Grouped Feature selection with a linear model

Grouped Feature selection with a linear model

Feature selection using the Sparse MMD OvA (MLP)

Feature selection using the Sparse MMD OvA (MLP)

Consensus clustering

Consensus clustering with linking constraints on sample pairs

Consensus clustering with linking constraints on sample pairs

Scoring with GEMINI

Scoring any model with GEMINI

Scoring any model with GEMINI

Trees

Building a differentiable unsupervised tree: DOUGLAS

Building a differentiable unsupervised tree: DOUGLAS

Building an unsupervised tree with kernel-kmeans objective: KAURI

Building an unsupervised tree with kernel-kmeans objective: KAURI