Angielski
Dimensionality Reduction in Machine Learning provides a comprehensive tutorial on dimension reduction algorithms as the first step of the data life cycle in a machine learning project. This book covers both the mathematical and programming sides of dimension reduction algorithms and compares dimension reduction algorithms in various aspects. Dimension reduction and feature selection is the first step in nearly every machine learning project. The authors provide readers with in-depth understanding of the foundational underpinnings as well as the methods of creating and applying dimension reduction algorithms. The book is divided into four Parts, with chapters from the leading researchers and experts in the field. Part One provides an Introduction to Machine Learning and the Data Life Cycle, with chapters covering the basic concepts of Machine Learning, essential mathematics for Machine Learning, and the methods and concepts of Feature Selection. Part Two covers Linear Methods for Dimension Reduction, with chapters on Principal Component Analysis and Linear Discriminant Analysis. Part Three covers Non-Linear Methods for Dimension Reduction, with chapters on Linear Local Embedding, Multi-dimensional Scaling, and t-distributed Stochastic Neighbor Embedding. Part Four covers Deep Learning Methods for Dimension Reduction, with chapters on Feature Extraction and Deep Learning, Autoencoders, and Dimensionality reduction in deep learning through group actions. With this stepwise structure and the applied code examples, readers become able to apply dimension reduction algorithms to different types of data, including tabular, text, and image data.
