Name
Abstract | ||
|---|---|---|
Given iid observations from an unknown absolute continuous distribution defined on some domain Omega, we propose a nonparametric method to learn a piecewise constant function to approximate the underlying probability density function. Our density estimate is a piecewise constant function defined on a binary partition of Omega. The key ingredient of the algorithm is to use discrepancy, a concept originates from Quasi Monte Carlo analysis, to control the partition process. The resulting algorithm is simple, efficient, and has a provable convergence rate. We empirically demonstrate its efficiency as a density estimation method. We also show how it can be utilized to find good initializations for k-means. |
Year | Venue | Field |
|---|---|---|
2016 | ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 29 (NIPS 2016) | Density estimation,Mathematical optimization,Quasi-Monte Carlo method,Constant function,Nonparametric statistics,Rate of convergence,Partition (number theory),Probability density function,Mathematics,Piecewise |
DocType | Volume | ISSN |
Conference | 29 | 1049-5258 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Li, Dangna | 1 | 0 | 0.68 |
| Kun Yang | 2 | 64 | 18.24 |
| Wing Hung Wong | 3 | 607 | 96.45 |