1st Edition

Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization

    174 Pages 46 B/W Illustrations
    by CRC Press

    174 Pages 46 B/W Illustrations
    by CRC Press

    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.


    • 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.


      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



    B.K. Tripathy, Anveshrithaa Sundareswaran, Shrusti Ghela