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Elements of dimensionality reduction and manifold learning / / Benyamin Ghojogh [and three others]
| Elements of dimensionality reduction and manifold learning / / Benyamin Ghojogh [and three others] |
| Autore | Ghojogh Benyamin |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer Nature Switzerland AG, , [2023] |
| Descrizione fisica | 1 online resource (617 pages) |
| Disciplina | 519.536 |
| Soggetto topico |
Dimension reduction (Statistics)
Machine learning |
| ISBN | 3-031-10602-4 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1: Introduction -- Part 1: Preliminaries and Background -- Chapter 2: Background on Linear Algebra -- Chapter 3: Background on Kernels -- Chapter 4: Background on Optimization -- Part 2: Spectral dimensionality Reduction -- Chapter 5: Principal Component Analysis -- Chapter 6: Fisher Discriminant Analysis -- Chapter 7: Multidimensional Scaling, Sammon Mapping, and Isomap -- Chapter 8: Locally Linear Embedding -- Chapter 9: Laplacian-based Dimensionality Reduction -- Chapter 10: Unified Spectral Framework and Maximum Variance Unfolding -- Chapter 11: Spectral Metric Learning -- Part 3: Probabilistic Dimensionality Reduction -- Chapter 12: Factor Analysis and Probabilistic Principal Component Analysis -- Chapter 13: Probabilistic Metric Learning -- Chapter 14: Random Projection -- Chapter 15: Sufficient Dimension Reduction and Kernel Dimension Reduction -- Chapter 16: Stochastic Neighbour Embedding -- Chapter 17: Uniform Manifold Approximation and Projection (UMAP) -- Part 4: Neural Network-based Dimensionality Reduction -- Chapter 18: Restricted Boltzmann Machine and Deep Belief Network -- Chapter 19: Deep Metric Learning -- Chapter 20: Variational Autoencoders -- Chapter 21: Adversarial Autoencoders. |
| Record Nr. | UNISA-996546839703316 |
Ghojogh Benyamin
|
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| Cham, Switzerland : , : Springer Nature Switzerland AG, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Elements of Dimensionality Reduction and Manifold Learning / / by Benyamin Ghojogh, Mark Crowley, Fakhri Karray, Ali Ghodsi
| Elements of Dimensionality Reduction and Manifold Learning / / by Benyamin Ghojogh, Mark Crowley, Fakhri Karray, Ali Ghodsi |
| Autore | Ghojogh Benyamin |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (617 pages) |
| Disciplina | 519.536 |
| Soggetto topico |
Artificial intelligence
Artificial Intelligence |
| ISBN |
9783031106026
3031106024 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1: Introduction -- Part 1: Preliminaries and Background -- Chapter 2: Background on Linear Algebra -- Chapter 3: Background on Kernels -- Chapter 4: Background on Optimization -- Part 2: Spectral dimensionality Reduction -- Chapter 5: Principal Component Analysis -- Chapter 6: Fisher Discriminant Analysis -- Chapter 7: Multidimensional Scaling, Sammon Mapping, and Isomap -- Chapter 8: Locally Linear Embedding -- Chapter 9: Laplacian-based Dimensionality Reduction -- Chapter 10: Unified Spectral Framework and Maximum Variance Unfolding -- Chapter 11: Spectral Metric Learning -- Part 3: Probabilistic Dimensionality Reduction -- Chapter 12: Factor Analysis and Probabilistic Principal Component Analysis -- Chapter 13: Probabilistic Metric Learning -- Chapter 14: Random Projection -- Chapter 15: Sufficient Dimension Reduction and Kernel Dimension Reduction -- Chapter 16: Stochastic Neighbour Embedding -- Chapter 17: Uniform Manifold Approximation and Projection (UMAP) -- Part 4: Neural Network-based Dimensionality Reduction -- Chapter 18: Restricted Boltzmann Machine and Deep Belief Network -- Chapter 19: Deep Metric Learning -- Chapter 20: Variational Autoencoders -- Chapter 21: Adversarial Autoencoders. |
| Record Nr. | UNINA-9910647787703321 |
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Ghojogh Benyamin
|
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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