The Nystrom sampling provides an efficient approach for large scale clustering problems, by generating a low-rank matrix approximation. However, existing sampling methods are limited by their accuracies and computing times. Here we propose a scalable Nystrom-based clustering algorithm with a new sampling procedure, called: Minimum Sum of Squared Similarities (MSSS).
Publication
- Bouneffouf D., Birol I.: Sampling with Minimum Sum of Squared Similarities for Nystrom-Based Large Scale Spectral Clustering. Proceedings of the Twenty-Fourth international joint conference on Artificial Intelligence (IJCAI) , 2015-July.
Current Release
Released Jun 05, 2015
Sampling with Minimum Sum of Squared Similarities for Nystrom-Based Large Scale Spectral Clustering
All Releases
| Version | Released | Description | Licenses | Status |
|---|---|---|---|---|
| 1.0 | Jun 05, 2015 | Sampling with Minimum Sum of Squared Similarities for Nystrom-Based Large Scale Spectral Clustering | BCCA (academic use) | final |