Name
Abstract | ||
|---|---|---|
Consider a weighted or unweighted k-nearest neighbor graph that has been built on n data points drawn randomly according to some density p on R^d. We study the convergence of the shortest path distance in such graphs as the sample size tends to infinity. We prove that for unweighted kNN graphs, this distance converges to an unpleasant distance function on the underlying space whose properties are detrimental to machine learning. We also study the behavior of the shortest path distance in weighted kNN graphs. |
Year | Venue | Field |
|---|---|---|
2012 | ICML | Indifference graph,Chordal graph,Artificial intelligence,Shortest Path Faster Algorithm,Longest path problem,K shortest path routing,k-nearest neighbors algorithm,Discrete mathematics,Combinatorics,Shortest path problem,Distance,Machine learning,Mathematics |
DocType | Citations | PageRank |
Conference | 9 | 0.60 |
References | Authors | |
5 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Morteza Alamgir | 1 | 97 | 5.83 |
| von luxburg | 2 | 3246 | 170.11 |