all AI news
k-Center Clustering with Outliers in Sliding Windows. (arXiv:2201.02448v1 [cs.LG])
Jan. 10, 2022, 2:10 a.m. | Paolo Pellizzoni, Andrea Pietracaprina, Geppino Pucci
cs.LG updates on arXiv.org arxiv.org
Metric $k$-center clustering is a fundamental unsupervised learning
primitive. Although widely used, this primitive is heavily affected by noise in
the data, so that a more sensible variant seeks for the best solution that
disregards a given number $z$ of points of the dataset, called outliers. We
provide efficient algorithms for this important variant in the streaming model
under the sliding window setting, where, at each time step, the dataset to be
clustered is the window $W$ of the most …
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
Senior ML Researcher - 3D Geometry Processing | 3D Shape Generation | 3D Mesh Data
@ Promaton | Europe
Senior Manager, IT Ops & Service Management, AI/ML
@ Sephora | San Francisco, CA, US, 50302863
AI/ML Senior Software Engineer (Indonesia)
@ Bjak | Jakarta, Jakarta, Indonesia
Data Engineer
@ Accenture Federal Services | Laurel, MD
Principal Engineer, Deep Learning
@ Outrider | Montreal, Quebec
Consultant Data manager F/H
@ Atos | Bezons, FRANCE, FR, 95870