Large-Scale High-Dimensional Clustering with Fast Sketching
Résumé
In this paper, we address the problem of high-dimensional k-means clustering in a large-scale setting, i.e. for datasets that comprise a large number of items. Sketching techniques have already been used to deal with this “large-scale” issue, by compressing the whole dataset into a single vector of random nonlinear generalized moments from which the k centroids are then retrieved efficiently. However , this approach usually scales quadratically with the dimension; to cope with high-dimensional datasets, we show how to use fast structured random matrices to compute the sketching operator efficiently. This yields significant speed-ups and memory savings for high-dimensional data, while the clustering results are shown to be much more stable, both on artificial and real datasets.
| Origine | Fichiers produits par l'(les) auteur(s) |
|---|---|
| Licence |
![]()
Référence hal-02960718 Logiciel Nicolas Keriven. SketchMLbox -- A MATLAB toolbox for large-scale mixture learning. 2018, ⟨swh:1:dir:a76f6aeb81ce73e178b8b822029d2c721ba24117;origin=https://hal.archives-ouvertes.fr/hal-02960718;visit=swh:1:snp:55fd14b490c1aef81d2582d91d911a71d0a159cf;anchor=swh:1:rev:d8de5cbab0e40297dd0d03d5c93895c568a30a99;path=/⟩. ⟨hal-02960718⟩