Name
Abstract | ||
|---|---|---|
In this paper, a new method to estimate the intrinsic dimensionality of high dimensional dataset is proposed. Based on neighborhood graph, our method calculates the non-negative weight coefficients from its neighbors for each data point and the numbers of those dominant positive weights in reconstructing coefficients are regarded as a faithful guide to the intrinsic dimensionality of dataset. The proposed method requires no parametric assumption on data distribution and is easy to implement in the general framework of manifold learning. Experimental results on several synthesized datasets and real datasets have shown the facility of our method. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/CIS.2007.158 | IJPRAI |
Keywords | Field | DocType |
data security,principal component analysis,data analysis,manifold learning,convex hull,data visualization,computational intelligence,telecommunications | Data set,Convex hull,Simplex,Curse of dimensionality,Parametric statistics,Artificial intelligence,Nonlinear dimensionality reduction,Instrumental and intrinsic value,Machine learning,Mathematics | Conference |
Volume | Issue | ISBN |
23 | 1 | 0-7695-3072-9 |
Citations | PageRank | References |
1 | 0.36 | 23 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chun-Guang Li | 1 | 310 | 17.35 |
| Jun Guo | 2 | 1579 | 137.24 |
| Xiangfei Nie | 3 | 4 | 0.75 |