Approximating L1-distances between mixture distributions using random projections
| dc.creator | Mahalanabis, Satyaki | |
| dc.creator | Stefankovic, Daniel | |
| dc.date | 2008-04-08 | |
| dc.date.accessioned | 2026-07-07T09:30:59Z | |
| dc.date.available | 2026-07-07T09:30:59Z | |
| dc.description | We consider the problem of computing L1-distances between every pair ofcprobability densities from a given family. We point out that the technique of Cauchy random projections (Indyk'06) in this context turns into stochastic integrals with respect to Cauchy motion. For piecewise-linear densities these integrals can be sampled from if one can sample from the stochastic integral of the function x->(1,x). We give an explicit density function for this stochastic integral and present an efficient sampling algorithm. As a consequence we obtain an efficient algorithm to approximate the L1-distances with a small relative error. For piecewise-polynomial densities we show how to approximately sample from the distributions resulting from the stochastic integrals. This also results in an efficient algorithm to approximate the L1-distances, although our inability to get exact samples worsens the dependence on the parameters. | |
| dc.identifier | https://arxiv.org/abs/0804.1170 | |
| dc.identifier | http://arxiv.org/abs/0804.1170 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/158306 | |
| dc.subject | Data Structures and Algorithms | |
| dc.title | Approximating L1-distances between mixture distributions using random projections | |
| dc.type | text |