Name
Abstract | ||
|---|---|---|
We develop an approach to intrinsic dimension estimation based on k-nearest neighbor (kNN) distances. The dimension estimator is derived using a general theory on functionals of kNN density estimates. This enables us to predict the performance of the dimension estimation algorithm. In addition, it allows for optimization of free parameters in the algorithm. We validate our theory through simulations and compare our estimator to previous kNN based dimensionality estimation approaches. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICASSP.2010.5494931 | 2010 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING |
Keywords | Field | DocType |
intrinsic dimension, manifold learning, k nearest neighbor, kNN density estimation, geodesics | Graph theory,k-nearest neighbors algorithm,Pattern recognition,Computer science,Nearest neighbor graph,Curse of dimensionality,Intrinsic dimension,Artificial intelligence,Estimation theory,Nonlinear dimensionality reduction,Estimator | Conference |
ISSN | Citations | PageRank |
1520-6149 | 4 | 0.51 |
References | Authors | |
3 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kumar Sricharan | 1 | 57 | 6.62 |
| Raviv Raich | 2 | 432 | 58.13 |
| Alfred O. Hero III | 3 | 2600 | 301.12 |