Sparse and geometry-aware generalisation of the mutual information for joint discriminative clustering and feature selection
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2023
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866909260849348608 |
|---|---|
| author | Ohl, Louis Mattei, Pierre-Alexandre Bouveyron, Charles Leclercq, Mickaël Droit, Arnaud Precioso, Frédéric |
| author_facet | Ohl, Louis Mattei, Pierre-Alexandre Bouveyron, Charles Leclercq, Mickaël Droit, Arnaud Precioso, Frédéric |
| contents | Feature selection in clustering is a hard task which involves simultaneously the discovery of relevant clusters as well as relevant variables with respect to these clusters. While feature selection algorithms are often model-based through optimised model selection or strong assumptions on the data distribution, we introduce a discriminative clustering model trying to maximise a geometry-aware generalisation of the mutual information called GEMINI with a simple l1 penalty: the Sparse GEMINI. This algorithm avoids the burden of combinatorial feature subset exploration and is easily scalable to high-dimensional data and large amounts of samples while only designing a discriminative clustering model. We demonstrate the performances of Sparse GEMINI on synthetic datasets and large-scale datasets. Our results show that Sparse GEMINI is a competitive algorithm and has the ability to select relevant subsets of variables with respect to the clustering without using relevance criteria or prior hypotheses. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2302_03391 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Sparse and geometry-aware generalisation of the mutual information for joint discriminative clustering and feature selection Ohl, Louis Mattei, Pierre-Alexandre Bouveyron, Charles Leclercq, Mickaël Droit, Arnaud Precioso, Frédéric Machine Learning Artificial Intelligence Computation Methodology 62H30 G.3 Feature selection in clustering is a hard task which involves simultaneously the discovery of relevant clusters as well as relevant variables with respect to these clusters. While feature selection algorithms are often model-based through optimised model selection or strong assumptions on the data distribution, we introduce a discriminative clustering model trying to maximise a geometry-aware generalisation of the mutual information called GEMINI with a simple l1 penalty: the Sparse GEMINI. This algorithm avoids the burden of combinatorial feature subset exploration and is easily scalable to high-dimensional data and large amounts of samples while only designing a discriminative clustering model. We demonstrate the performances of Sparse GEMINI on synthetic datasets and large-scale datasets. Our results show that Sparse GEMINI is a competitive algorithm and has the ability to select relevant subsets of variables with respect to the clustering without using relevance criteria or prior hypotheses. |
| title | Sparse and geometry-aware generalisation of the mutual information for joint discriminative clustering and feature selection |
| topic | Machine Learning Artificial Intelligence Computation Methodology 62H30 G.3 |
| url | https://arxiv.org/abs/2302.03391 |