Representation in Machine Learning

This book provides a comprehensive guide to representation, which is essential for Machine Learning, and covers a wide range of techniques such as Locality Sensitive Hashing, Distance Metrics, Principal Components, Random Projections, and Autoencoders. It also includes experimental results to demonstrate their effectiveness.

Format: Paperback / softback
Length: 93 pages
Publication date: 21 January 2023
Publisher: Springer Verlag, Singapore

This book is a comprehensive guide to representation, which forms the core of Machine Learning (ML). It addresses the challenges of analyzing high-dimensional data, which are prevalent in state-of-the-art practical applications. Despite the popularity of many ML algorithms, they often fail to perform effectively when dealing with large datasets. This book provides solutions to this problem, covering a wide range of representation techniques that are essential for both academics and ML practitioners. These techniques include Locality Sensitive Hashing (LSH), Distance Metrics and Fractional Norms, Principal Components (PCs), Random Projections, and Autoencoders. The book includes experimental results to demonstrate the effectiveness of these techniques.

The importance of representation in ML cannot be overstated. It plays a crucial role in the ability of machines to learn and make predictions from data. High-dimensional data, such as images, videos, and text, can be difficult to process and analyze using traditional ML algorithms. Representation techniques, such as LSH, Distance Metrics, and Fractional Norms, help to reduce the dimensionality of the data, making it more manageable and interpretable.

Principal Components (PCs) is a widely used representation technique that captures the most important features of the data while reducing the dimensionality. It is achieved by projecting the data onto a lower-dimensional space using a linear transformation, which is learned from the data. PCs are useful for dimensionality reduction, feature extraction, and data visualization.

Random Projections is another technique that is used to reduce the dimensionality of data. It involves projecting the data onto a lower-dimensional space using random vectors, which are generated from a random distribution. Random projections are useful for preserving the structure of the data while reducing the dimensionality.

Autoencoders are a type of neural network that is used for dimensionality reduction and feature extraction. They learn to reconstruct the input data from a lower-dimensional representation, which is learned during the training process. Autoencoders are useful for data compression, anomaly detection, and image reconstruction.

In addition to these representation techniques, the book also covers a wide range of practical applications of ML, such as image classification, object detection, and speech recognition. It provides detailed explanations of the algorithms and techniques used in these applications, as well as examples of their use in real-world scenarios.

Overall, this book is a valuable resource for anyone interested in ML and representation. It provides a comprehensive and up-to-date introduction to the field, covering a wide range of topics and providing practical examples and insights. Whether you are a researcher, practitioner, or student, this book will help you to understand the importance of representation in ML and to develop effective techniques for analyzing high-dimensional data.

Weight: 174g
Dimension: 235 x 155 (mm)
ISBN-13: 9789811979071
Edition number: 1st ed. 2023