Free Delivery Over $100
|
|
|||
|
||||
OverviewUnsupervised 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. Full Product DetailsAuthor: B.K. Tripathy (Vellore Institute of Technology, Vellore, India.) , Anveshrithaa Sundareswaran (Vellore Institute of Technology, India.) , Shrusti Ghela (Vellore Institute of Technology, India.)Publisher: Taylor & Francis Ltd Imprint: CRC Press Weight: 0.390kg ISBN: 9781032041018ISBN 10: 1032041013 Pages: 160 Publication Date: 02 September 2021 Audience: Professional and scholarly , General/trade , Professional & Vocational , General Format: Hardback Publisher's Status: Active Availability: In Print  This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us. Table of ContentsChapter 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 TechniquesReviewsAuthor InformationB.K. Tripathy, Anveshrithaa Sundareswaran, Shrusti Ghela Tab Content 6Author Website:Countries AvailableAll regions |
||||
