Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization
Seiten
2023
CRC Press (Verlag)
978-1-032-04103-2 (ISBN)
CRC Press (Verlag)
978-1-032-04103-2 (ISBN)
This book describes algorithms like Locally Linear Embedding, Laplacian eigenmaps, Semidefinite Embedding, t-SNE to resolve the problem of dimensionality reduction in case of non-linear relationships within the data. Underlying mathematical concepts, derivations, proofs, strengths and limitations of these algorithms are discussed as well.
Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization describes such algorithms as Locally Linear Embedding (LLE), Laplacian Eigenmaps, Isomap, Semidefinite Embedding, and t-SNE to resolve the problem of dimensionality reduction in the case of non-linear relationships within the data. Underlying mathematical concepts, derivations, and proofs with logical explanations for these algorithms are discussed, including strengths and limitations. The book highlights important use cases of these algorithms and provides examples along with visualizations. Comparative study of the algorithms is presented to give a clear idea on selecting the best suitable algorithm for a given dataset for efficient dimensionality reduction and data visualization.
FEATURES
Demonstrates how unsupervised learning approaches can be used for dimensionality reduction
Neatly explains algorithms with a focus on the fundamentals and underlying mathematical concepts
Describes the comparative study of the algorithms and discusses when and where each algorithm is best suitable for use
Provides use cases, illustrative examples, and visualizations of each algorithm
Helps visualize and create compact representations of high dimensional and intricate data for various real-world applications and data analysis
This book is aimed at professionals, graduate students, and researchers in Computer Science and Engineering, Data Science, Machine Learning, Computer Vision, Data Mining, Deep Learning, Sensor Data Filtering, Feature Extraction for Control Systems, and Medical Instruments Input Extraction.
Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization describes such algorithms as Locally Linear Embedding (LLE), Laplacian Eigenmaps, Isomap, Semidefinite Embedding, and t-SNE to resolve the problem of dimensionality reduction in the case of non-linear relationships within the data. Underlying mathematical concepts, derivations, and proofs with logical explanations for these algorithms are discussed, including strengths and limitations. The book highlights important use cases of these algorithms and provides examples along with visualizations. Comparative study of the algorithms is presented to give a clear idea on selecting the best suitable algorithm for a given dataset for efficient dimensionality reduction and data visualization.
FEATURES
Demonstrates how unsupervised learning approaches can be used for dimensionality reduction
Neatly explains algorithms with a focus on the fundamentals and underlying mathematical concepts
Describes the comparative study of the algorithms and discusses when and where each algorithm is best suitable for use
Provides use cases, illustrative examples, and visualizations of each algorithm
Helps visualize and create compact representations of high dimensional and intricate data for various real-world applications and data analysis
This book is aimed at professionals, graduate students, and researchers in Computer Science and Engineering, Data Science, Machine Learning, Computer Vision, Data Mining, Deep Learning, Sensor Data Filtering, Feature Extraction for Control Systems, and Medical Instruments Input Extraction.
B.K. Tripathy, Anveshrithaa Sundareswaran, Shrusti Ghela
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
| Erscheinungsdatum | 06.09.2021 |
|---|---|
| Zusatzinfo | 46 Line drawings, black and white; 46 Illustrations, black and white |
| Verlagsort | London |
| Sprache | englisch |
| Maße | 156 x 234 mm |
| Gewicht | 322 g |
| Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
| Mathematik / Informatik ► Informatik ► Theorie / Studium | |
| Technik ► Umwelttechnik / Biotechnologie | |
| Wirtschaft ► Volkswirtschaftslehre ► Ökonometrie | |
| ISBN-10 | 1-032-04103-X / 103204103X |
| ISBN-13 | 978-1-032-04103-2 / 9781032041032 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
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