Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization
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Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization
Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization
Tripathy, B.K.; Ghela, Shrusti; Sundareswaran, Anveshrithaa
Taylor & Francis Ltd
09/2023
160
Mole
Inglês
9781032041032
15 a 20 dias
Descrição não disponível.
Chapter 1 Introduction to Dimensionality Reduction
Chapter 2 Principal Component Analysis (PCA)
Chapter 3 Dual PCA
Chapter 4 Kernel PCA
Chapter 5 Canonical Correlation Analysis (CCA
Chapter 6 Multidimensional Scaling (MDS)
Chapter 7 Isomap
Chapter 8 Random Projections
Chapter 9 Locally Linear Embedding
Chapter 10 Spectral Clustering
Chapter 11 Laplacian Eigenmap
Chapter 12 Maximum Variance Unfolding
Chapter 13 t-Distributed Stochastic Neighbor Embedding (t-SNE
Chapter 14 Comparative Analysis of Dimensionality Reduction
Techniques
Chapter 2 Principal Component Analysis (PCA)
Chapter 3 Dual PCA
Chapter 4 Kernel PCA
Chapter 5 Canonical Correlation Analysis (CCA
Chapter 6 Multidimensional Scaling (MDS)
Chapter 7 Isomap
Chapter 8 Random Projections
Chapter 9 Locally Linear Embedding
Chapter 10 Spectral Clustering
Chapter 11 Laplacian Eigenmap
Chapter 12 Maximum Variance Unfolding
Chapter 13 t-Distributed Stochastic Neighbor Embedding (t-SNE
Chapter 14 Comparative Analysis of Dimensionality Reduction
Techniques
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manifold learning;nonlinear data analysis;spectral methods;feature extraction techniques;sensor data processing;advanced machine learning methods;high-dimensional data visualization techniques
Chapter 1 Introduction to Dimensionality Reduction
Chapter 2 Principal Component Analysis (PCA)
Chapter 3 Dual PCA
Chapter 4 Kernel PCA
Chapter 5 Canonical Correlation Analysis (CCA
Chapter 6 Multidimensional Scaling (MDS)
Chapter 7 Isomap
Chapter 8 Random Projections
Chapter 9 Locally Linear Embedding
Chapter 10 Spectral Clustering
Chapter 11 Laplacian Eigenmap
Chapter 12 Maximum Variance Unfolding
Chapter 13 t-Distributed Stochastic Neighbor Embedding (t-SNE
Chapter 14 Comparative Analysis of Dimensionality Reduction
Techniques
Chapter 2 Principal Component Analysis (PCA)
Chapter 3 Dual PCA
Chapter 4 Kernel PCA
Chapter 5 Canonical Correlation Analysis (CCA
Chapter 6 Multidimensional Scaling (MDS)
Chapter 7 Isomap
Chapter 8 Random Projections
Chapter 9 Locally Linear Embedding
Chapter 10 Spectral Clustering
Chapter 11 Laplacian Eigenmap
Chapter 12 Maximum Variance Unfolding
Chapter 13 t-Distributed Stochastic Neighbor Embedding (t-SNE
Chapter 14 Comparative Analysis of Dimensionality Reduction
Techniques
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
