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Beschreibung
The proposed work developed to reduce the risk of high dimensional data representation in the form of low dimensional data representation. Reduction in dimensionality is achieved by choosing the right features where the dimensions get reduced, it build a right model to achieve right choice. This report presents the dimensionality reduction practices such as Principal Component Analysis (PCA), Kernel PCA and Locally Linear Embedded (LLE).
USING PCA, KPCA AND LLE
Details
| Verlag | LAP LAMBERT Academic Publishing |
| Ersterscheinung | 28. Juli 2021 |
| Maße | 22 cm x 15 cm x 0.4 cm |
| Gewicht | 96 Gramm |
| Format | Softcover |
| ISBN-13 | 9786204184777 |
| Seiten | 52 |
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