Clustering mixture models in almost-linear time via list-decodable mean estimation

Author:

Diakonikolas Ilias1,Kane Daniel M.2,Kongsgaard Daniel2,Li Jerry3,Tian Kevin4

Affiliation:

1. University of Wisconsin-Madison, USA

2. University of California at San Diego, USA

3. Microsoft Research, USA

4. Stanford University, USA

Publisher

ACM

Reference91 articles.

1. Sample-Optimal Density Estimation in Nearly-Linear Time

2. Jayadev Acharya Ashkan Jafarpour Alon Orlitsky and Ananda Theertha Suresh. 2014. Near-optimal-sample estimators for spherical gaussian mixtures. arXiv preprint arXiv:1402.4746. Jayadev Acharya Ashkan Jafarpour Alon Orlitsky and Ananda Theertha Suresh. 2014. Near-optimal-sample estimators for spherical gaussian mixtures. arXiv preprint arXiv:1402.4746.

3. Database-friendly random projections: Johnson-Lindenstrauss with binary coins

4. On Spectral Learning of Mixtures of Distributions

5. Joseph Anderson , Mikhail Belkin , Navin Goyal , Luis Rademacher , and James Voss . 2014 . The more, the merrier: the blessing of dimensionality for learning large gaussian mixtures . In Conference on Learning Theory. 1135–1164 . Joseph Anderson, Mikhail Belkin, Navin Goyal, Luis Rademacher, and James Voss. 2014. The more, the merrier: the blessing of dimensionality for learning large gaussian mixtures. In Conference on Learning Theory. 1135–1164.

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