Representation in Machine Learning

✍ Scribed by Murty, M N; Avinash, M

• Publisher: Springer
• Year: 2023
• Tongue: English
• Leaves: 101
• Edition: 1
• Category: Library

✦ Synopsis

This book provides a concise but comprehensive guide to representation, which forms the core of Machine Learning (ML). State-of-the-art practical applications involve a number of challenges for the analysis of high-dimensional data. Unfortunately, many popular ML algorithms fail to perform, in both theory and practice, when they are confronted with the huge size of the underlying data. Solutions to this problem are aptly covered in the book.

In addition, the book covers a wide range of representation techniques that are important for academics and ML practitioners alike, such as Locality Sensitive Hashing (LSH), Distance Metrics and Fractional Norms, Principal Components (PCs), Random Projections and Autoencoders. Several experimental results are provided in the book to demonstrate the discussed techniques’ effectiveness.

✦ Table of Contents

Preface
Overview
Audience
Organization
Contents
Acronyms
1 Introduction
1.1 Machine Learning (ML) System
1.2 Main Steps in an ML System
1.2.1 Data Collection/Acquisition
1.2.2 Feature Engineering and Representation
1.2.3 Model Selection
1.2.4 Model Estimation
1.2.5 Model Validation
1.2.6 Model Explanation
1.3 Data Sets Used
1.4 Summary
References
2 Representation
2.1 Introduction
2.2 Representation in Problem Solving
2.3 Representation of Data Items
2.4 Representation of Classes
2.5 Representation of Clusters
2.6 Summary
References
3 Nearest Neighbor Algorithms
3.1 Introduction
3.2 Nearest Neighbors in High-Dimensional Spaces
3.3 Fractional Norms
3.4 Locality Sensitive Hashing (LSH) and Applications
3.5 Summary
References
4 Representation Using Linear Combinations
4.1 Introduction
4.2 Feature Selection
4.3 Principal Component Analysis
4.4 Random Projections
4.5 Non-negative Matrix Factorization
4.6 Summary
References
5 Non-linear Schemes for Representation
5.1 Introduction
5.2 Optimization Schemes for Representation
5.3 Visualization
5.4 Autoencoders for Representation
5.5 Experimental Results: ORL Data Set
5.6 Experimental Results: MNIST Data Set
5.7 Summary
References
6 Conclusions
References
Index