Name
Abstract | ||
|---|---|---|
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. Its advantages include 1) reduced storage complexity and 2) 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 characteri... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TSP.2019.2924585 | IEEE Transactions on Signal Processing |
Keywords | Field | DocType |
Approximation algorithms,Message passing,Clustering algorithms,Signal processing algorithms,Computational complexity,Matching pursuit algorithms | Mathematical optimization,Algorithm,Empirical characteristic function,Sampling (statistics),Cluster analysis,Sample complexity,Centroid,Message passing,Mathematics | Journal |
Volume | Issue | ISSN |
67 | 17 | 1053-587X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Evan Bryne | 1 | 0 | 0.34 |
| Antoine Chatalic | 2 | 1 | 0.70 |
| R. Gribonval | 3 | 2347 | 282.40 |
| Philip Schniter | 4 | 1620 | 93.74 |