| Autor: |
Byrne, Evan, Chatalic, Antoine, Gribonval, Remi, Schniter, Philip |
| Rok vydání: |
2017 |
| Předmět: |
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| Druh dokumentu: |
Working Paper |
| Popis: |
In sketched clustering, a dataset of $T$ samples is first sketched down to a vector of modest size, from which the centroids are subsequently extracted. Advantages include i) reduced storage complexity and ii) centroid extraction complexity independent of $T$. For the sketching methodology recently proposed by Keriven, et al., which can be interpreted as a random sampling of the empirical characteristic function, we propose a sketched clustering algorithm based on approximate message passing. Numerical experiments suggest that our approach is more efficient than the state-of-the-art sketched clustering algorithm "CL-OMPR" (in both computational and sample complexity) and more efficient than k-means++ when $T$ is large. |
| Databáze: |
arXiv |
| Externí odkaz: |
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