Name
Abstract | ||
|---|---|---|
In this paper, we propose an adaptive incremental nonlinear dimensionality reduction algorithm for data stream in adaptive Self-organizing Isometric Embedding [1][3] framework. Assuming that each sampling point of underlying manifold and its adaptive neighbors [3] can preserve the principal directions of the regions that they reside on, our algorithm need only update the geodesic distances between anchors and all the other points, as well as distances between neighbors of incremental points and all the other points when a new point arrives. Under the above assumption, our algorithms can realize an approximate linear time complexity embedding of incremental points and effectively tradeoff embedding precision and time cost. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11893028_109 | ICONIP |
Keywords | Field | DocType |
null | Topology,Dimensionality reduction,Embedding,Data stream,Computer science,Time complexity,Artificial neural network,Nonlinear dimensionality reduction,Geodesic,Manifold | Conference |
Volume | Issue | ISSN |
4232 LNCS | null | 0302-9743 |
ISBN | Citations | PageRank |
3-540-46479-4 | 1 | 0.52 |
References | Authors | |
10 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuexian Hou | 1 | 269 | 38.59 |
| Gong Kefei | 2 | 1 | 0.52 |
| Pilian He | 3 | 29 | 7.46 |