Dimensionality Reduction Methods: Comparative Analysis of methods PCA, PPCA and KPCA
保存先:
| 著者: | |
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| フォーマット: | artículo original |
| 状態: | Versión publicada |
| 出版日付: | 2016 |
| その他の書誌記述: | The dimensionality reduction methods are algorithms mapping the set of data in subspaces derived from the original space, of fewer dimensions, that allow a description of the data at a lower cost. Due to their importance, they are widely used in processes associated with learning machine. This article presents a comparative analysis of PCA, PPCA and KPCA dimensionality reduction methods. A reconstruction experiment of worm-shape data was performed through structures of landmarks located in the body contour, with methods having different number of main components. The results showed that all methods can be seen as alternative processes. Nevertheless, thanks to the potential for analysis in the features space and the method for calculation of its preimage presented, KPCA offers a better method for recognition process and pattern extraction |
| 国: | Portal de Revistas UNA |
| 機関: | Universidad Nacional de Costa Rica |
| Repositorio: | Portal de Revistas UNA |
| 言語: | Español |
| OAI Identifier: | oai:www.revistas.una.ac.cr:article/7586 |
| オンライン･アクセス: | https://www.revistas.una.ac.cr/index.php/uniciencia/article/view/7586 |
| キーワード: | Dimensionality Reduction Points Clouds Preimage problem Reducción de dimensionalidad nube de datos problema de la preimagen. |