Loading...
Please wait, while we are loading the content...
Similar Documents
High-dimensional approximate nearest neighbor : k-d Generalized Randomized Forests
| Content Provider | Semantic Scholar |
|---|---|
| Author | Velner, Mari Liis |
| Copyright Year | 2017 |
| Abstract | k-d trees are one of the most common data structures used in the optimization problem of Nearest Neighbour Search (NNS), which to this day has issues both theoretical and practical concerning the curse of dimensionality. The article “High-dimensional approximate nearest neighbor: k-d Generalized Randomized Forests“ presents a new data structure called generalized randomized k-d forest, k-d GeRaF that offers considerable competition to the current state-of-the-art algorithms for NNS. The authors prove that their method works efficiently on both synthetic and real datasets of a wide range of dimensions and sizes. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://courses.cs.ut.ee/MTAT.03.238/2017_fall/uploads/Main/Velner_report.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
