Simulation Research of Feature Extraction Method Based on Nonlinear Manifold Learning
| Autor: | Xiao Peng Xie, Hai Bing Xiao |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
Clustering high-dimensional data
Hessian matrix business.industry Dimensionality reduction Feature extraction Pattern recognition General Medicine Computer Science::Computational Geometry Statistics::Machine Learning Nonlinear manifold symbols.namesake Computer Science::Computer Vision and Pattern Recognition symbols Nonlinear feature extraction Artificial intelligence business Linear embedding Mathematics |
| Zdroj: | Applied Mechanics and Materials. 533:247-251 |
| ISSN: | 1662-7482 |
| Popis: | This paper deals with the study of Locally Linear Embedding (LLE) and Hessian LLE nonlinear feature extraction for high dimensional data dimension reduction. LLE and Hessian LLE algorithm which reveals the characteristics of nonlinear manifold learning were analyzed. LLE and Hessian LLE algorithm simulation research was studied through different kinds of sample for dimensionality reduction. LLE and Hessian LLE algorithm’s classification performance was compared in accordance with MDS. The simulation experimental results show that LLE and Hessian LLE are very effective feature extraction method for nonlinear manifold learning. |
| Databáze: | OpenAIRE |
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