Name
Abstract | ||
|---|---|---|
This paper proposes a new approach to analyze high-dimensional data set using low-dimensional manifold. This manifold-based approach provides a unified formulation for both learning from and synthesis back to the input space. The manifold learning method desires to solve two problems in many existing algorithms. The first problem is the local manifold distortion caused by the cost averaging of the global cost optimization during the manifold learning. The second problem results from the unit variance constraint generally used in those spectral embedding methods where global metric information is lost. For the out-of-sample data points, the proposed approach gives simple solutions to transverse between the input space and the feature space. In addition, this method can be used to estimate the underlying dimension and is robust to the number of neighbors. Experiments on both low-dimensional data and real image data are performed to illustrate the theory. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/TSMCB.2008.2007499 | IEEE Transactions on Systems, Man, and Cybernetics, Part B |
Keywords | Field | DocType |
high-dimensional data set,low-dimensional manifold,high-dimensional data,learning (artificial intelligence),index terms—dimensionality reduction,out-of-sample extension,dimensionality reduction,out-of-sample extension.,manifold-based approach,out-of-sample data point,real image data,learning and synthesis,manifold-based learning,out-of-sample data points,global cost optimization,local manifold distortion,feature space,input space,unit variance constraint,low-dimensional data,manifold learning,manifold-based synthesis,data analysis,indexing terms,high dimensional data,principal component analysis,computer science,nonlinear distortion,cost function,learning artificial intelligence,robustness,visual perception | Local tangent space alignment,Mathematical optimization,Feature vector,Dimensionality reduction,Computer science,Manifold alignment,Robustness (computer science),Artificial intelligence,Nonlinear dimensionality reduction,Manifold,Machine learning,Configuration space | Journal |
Volume | Issue | ISSN |
39 | 3 | 1941-0492 |
Citations | PageRank | References |
9 | 0.54 | 13 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dong Huang | 1 | 163 | 14.20 |
| Zhang Yi | 2 | 1765 | 194.41 |
| Xiaorong Pu | 3 | 85 | 11.17 |