Semi-Supervised and Unsupervised Machine Learning: Novel Strategies

Chapter 1

Introduction

The main objective of this book is to develop machine learning (ML) tools that help minimize the (costly) human supervision required for the analysis of large volumes of data. To address such an objective, the research work developed in this book focused on two major fields in ML: unsupervised and semi-supervised learning. Both ML areas have been widely used in a large number of applications such as the clustering and semi-automatic annotation of large datasets of documents and the dimensionality reduction of microarray matrices for the analysis and interpretation of genomic data. In these examples, owing to the complexity and/or size of the large amounts of data to be processed, a fully supervised analysis without the help of semi- or unsupervised ML tools would become prohibitive.

Thus, the first aim of this book focused on the development of new algorithms in the field of semi-supervised ML. Semi-supervised learning provides an alternative to fully supervised classification. In supervised classification, a so-called training phase is performed using only labeled data. Typically, the labels for the training observations are manually compiled by human annotators. Then, a supervised algorithm is capable of inferring prediction rules or models from the available training data and consequently delivering the most probable label for a new observation, not necessarily observed in the training data. However, a major limitation of supervised algorithms is related ...