Name
Title | ||
|---|---|---|
A Second-Order Mapping PCA Based Intrinsic Dimension Estimate for Nonlinear Time Series |
Abstract | ||
|---|---|---|
The delay vectors of nonlinear time series are mapped into a second-order space and the intrinsic dimension of the underlying dynamics is estimated by the saturation of the nonvanishing eigenvalues of the covariant matrix as the embedding dimension increases. This method needs less data and has certain noise resistant degree. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICNC.2008.119 | ICNC |
Keywords | Field | DocType |
embedding dimension increase,resistant degree,second-order space,nonvanishing eigenvalues,underlying dynamic,intrinsic dimension,nonlinear time series,delay vector,covariant matrix,intrinsic dimension estimate,certain noise,second-order mapping pca,covariance matrix,noise,time series,time series analysis,second order,correlation,principal component analysis | Mathematical optimization,Embedding,Nonlinear system,Covariant transformation,Mathematical analysis,Matrix (mathematics),Intrinsic dimension,Covariance matrix,Principal component analysis,Eigenvalues and eigenvectors,Mathematics | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaolin Huang | 1 | 0 | 1.01 |
| Xinbao Ning | 2 | 2 | 1.53 |