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Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization



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ISBN 9781032041018
September 8, 2021 Forthcoming by CRC Press
216 Pages 46 B/W Illustrations

 
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Book Description

This book describes algorithms like Locally Linear Embedding (LLE), Laplacian eigenmaps, Isomap, Semidefinite Embedding, 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 the limitations.  It highlights important use cases of these algorithms and few 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 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 aims 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.

Table of Contents

  1. Introduction to Dimensionality Reduction
    1. Introduction

  2. Principal Component Analysis
    1. Explanation and working
    2. Advantages and Limitations
    3. Use cases
    4. Examples and Tutorial

  3. Dual PCA
    1. Explanation and working

  4. Kernel PCA
    1. Explanation and working
    2. Advantages and Limitations
    3. Use cases
    4. Examples and Tutorial

  5. Canonical Correlation Analysis
    1. Explanation and working
    2. Advantages and Limitations
    3. Use cases and examples

  6. Multidimensional Scaling
    1. Explanation and working
    2. Advantages and Limitations
    3. Use cases
    4. Examples and Tutorial

  7. Isomap
    1. Explanation and working
    2. Advantages and Limitations
    3. Use cases
    4. Examples and Tutorial

  8. Random Projections
    1. Explanation and working
    2. Advantages and Limitations
    3. Use cases
    4. Examples and Tutorial

  9. Locally Linear Embedding
    1. Explanation and working
    2. Advantages and Limitations
    3. Use cases
    4. Examples and Tutorial

  10. Spectral Clustering
    1. Explanation and working
    2. Advantages and Limitations
    3. Use cases
    4. Examples and Tutorial

  11. Laplacian Eigenmap
    1. Explanation and working
    2. Advantages and Limitations
    3. Use cases
    4. Examples and Tutorial

  12. Maximum Variance Unfolding
    1. Explanation and working
    2. Advantages and Limitations
    3. Use cases

  13. t-distributed Stochastic Neighbor Embedding (t-SNE)
    1. Explanation and working
    2. Advantages and Limitations
    3. Use cases
    4. Examples and Tutorial

  14. Comparative Analysis of Dimensionality Reduction Techniques
    1. Introduction
    2. Convex Dimensionality reduction techniques
    3. Non-convex Dimensionality reduction techniques
    4. Comparison of Dimensionality reduction techniques
    5. Comparison of manifold learning techniques with example
    6. Discussion

...
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Author(s)

Biography

Dr. B. K. Tripathy, a distinguished researcher in Mathematics and Computer Science has more than 600 publications to his credit in international journals, conference proceedings, chapters in edited research volumes, edited volumes, monographs and books. He has supervised over 50 research degrees to his credit. He has a distinguished professional career of over 40 years of service in different positions and at present he is working as a professor (Higher Academic Grade) and Dean of school of Information technology in VIT, Vellore. As a student, Dr. Tripathy has won three gold medals, national scholarship for post graduate studies, UGC fellowship to pursue PhD, DST sponsorship to pursue M. Tech in computer science at Pune University and DOE visiting fellowship to IIT, Kharagpur. He was nominated as distinguished alumni of Berhampur University on its silver jubilee and golden jubilee years. For efficient service as a reviewer for Mathematical Reviews, he was selected as an honorary member of American Mathematical Society. Besides this he is a life member/senior member/member of over 20 international professional societies including IEEE, ACM, IRSS, CSI, Indian Science congress, IMS, IET, ACM Compute News group and IEANG. Dr. Tripathy is an editor/editorial board member/ reviewer of over 100 international journals like Information Sciences, IEEE transactions on Fuzzy Systems, Knowledge Based Systems, Applied Soft Computing, IEEE Access, Analysis of Neural Networks, Int. Jour. of Information Technology and Decision Making, Proceedings of the Royal Society-A and Kybernetes. He has so far adjudicated PhD theses of more than 20 universities all over India. He has organised many international conferences, workshops, FDPs, guest lectures, industrial visits, webinars over the years. Dr. Tripathy has to his credits delivered keynote speeches in international conferences, organised special sessions and chaired sessions. Also, many of his papers have been selected as best papers at international conferences. He has received funded projects from UGC, DST and DRDO and published some patents also.

Anveshrithaa S

Anveshrithaa Sundareswaran is a final year B. Tech (Computer Science) student at Vellore Institute of Technology, Vellore. Her areas of interest include machine learning, deep learning and data science. She has shown her research capabilities with several publications. Her research on Promoter Prediction in DNA Sequences of Escherichia coli using Machine Learning Algorithms won the best Student Paper award at the IEEE Madras section Student Paper Contest, 2019 and was published later in the International Journal of Scientific & Technology Research. She has presented a paper on "Real-Time Vehicle Traffic Analysis using Long Short-Term Memory Networks in Apache Spark" at the IEEE International Conference on Emerging Trends in Information Technology and Engineering, 2020. Her research on Real-Time Traffic Prediction using Ensemble Learning for Deep Neural Networks has been published in the International Journal of Intelligent Information Technologies (IJIIT). Also, she has communicated a research paper on Real-Time Weather Analytics using Long Short-Term Memory Networks to the International Journal of Cognitive Computing in Engineering. Her other achievements include the outstanding student award at the 2020 Tsinghua University Deep Learning Summer School where she was the only student to represent India. Achievers Award and Raman Research Award from VIT University are some of the other recognitions of her merit.

Shrusti Ghela

Shruthi Ghela has received her B. Tech (CS) degree from Vellore Institute of Technology, Vellore in May 2020. She completed her Capstone project at Iconflux Technologies Pvt. Ltd., Ahmedabad, India in the field of Machine Learning during. She has completed two summer projects: one in the domain of Data Science from KeenExpert Solution Pvt. Ltd., Ahmedabad, India and the other in Web Development from Jahannum.com, Ahmedabad, India. For her excellent academic performance, she received scholarship for all 4 years of under graduation from VIT. She was the winner of the DevJams'19 hackathon for two consecutive years in 2018 and 2019. She has proficiency in the languages German and Chinese besides English. In an attempt to increase and intensify her specialisations, she has completed IBM Data Science Professional Certificate (Coursera), Machine Learning A-Z (Udemy) and Machine Learning by Andrew Ng (Coursera). Ms. Ghela has the skill set of Hadoop, Python, MATLAB, R, Haskell, Object Oriented Programming, Full Stack development, Functional Programming and Statistics. Her research area of interest includes Data Science and Quantum Computing. Apart from being a hard-core subject learner, she enjoys Photography, playing Tennis, reading and traveling.