Aurelien Bellet

Metric Learning - Synthesis Lectures on Artificial Intelligence and Machine Learning Aurelien Bellet

Marc Notes: Part of: Synthesis digital library of engineering and computer science.; Includes bibliographical references (pages 115-138).; Abstract freely available;full-text restricted to subscribers or individual document purchasers.; Compendex; INSPEC; Google scholar; Google book search; Similarity between objects plays an important role in both human cognitive processes and artificial systems for recognition and categorization. How to appropriately measure such similarities for a given task is crucial to the performance of many machine learning, pattern recognition and data mining methods. This book is devoted to metric learning, a set of techniques to automatically learn similarity and distance functions from data that has attracted a lot of interest in machine learning and related fields in the past ten years. In this book, we provide a thorough review of the metric learning literature that covers algorithms, theory and applications for both numerical and structured data. We first introduce relevant definitions and classic metric functions, as well as examples of their use in machine learning and data mining. We then review a wide range of metric learning algorithms, starting with the simple setting of linear distance and similarity learning. We show how one may scale-up these methods to very large amounts of training data. To go beyond the linear case, we discuss methods that learn nonlinear metrics or multiple linear metrics throughout the feature space, and review methods for more complex settings such as multi-task and semi-supervised learning. Although most of the existing work has focused on numerical data, we cover the literature on metric learning for structured data like strings, trees, graphs and time series. In the more technical part of the book, we present some recent statistical frameworks for analyzing the generalization performance in metric learning and derive results for some of the algorithms presented earlier. Finally, we illustrate the relevance of metric learning in real-world problems through a series of successful applications to computer vision, bioinformatics and information retrieval.; Also available in print.; Mode of access: World Wide Web.; System requirements: Adobe Acrobat Reader.; Title from PDF title page (viewed on February 22, 2015). Publisher Marketing: Similarity between objects plays an important role in both human cognitive processes and artificial systems for recognition and categorization. How to appropriately measure such similarities for a given task is crucial to the performance of many machine learning, pattern recognition and data mining methods. This book is devoted to metric learning, a set of techniques to automatically learn similarity and distance functions from data that has attracted a lot of interest in machine learning and related fields in the past ten years. In this book, we provide a thorough review of the metric learning literature that covers algorithms, theory and applications for both numerical and structured data. We first introduce relevant definitions and classic metric functions, as well as examples of their use in machine learning and data mining. We then review a wide range of metric learning algorithms, starting with the simple setting of linear distance and similarity learning. We show how one may scale-up these methods to very large amounts of training data. To go beyond the linear case, we discuss methods that learn nonlinear metrics or multiple linear metrics throughout the feature space, and review methods for more complex settings such as multi-task and semi-supervised learning. Although most of the existing work has focused on numerical data, we cover the literature on metric learning for structured data like strings, trees, graphs and time series. In the more technical part of the book, we present some recent statistical frameworks for analyzing the generalization performance in metric learning and derive results for some of the algorithms presented earlier. Finally, we illustrate the relevance of metric learning in real-world problems through a series of successful applications to computer vision, bioinformatics and information retrieval.

Contributor Bio:  Bellet, Aurelien Aurelien Bellet received his Ph. D. in Machine Learning from the University of Saint-Etienne (France) in 2012. His work focused on algorithmic and theoretical aspects of metric and similarity learning. After completing his thesis, he was a postdoctoral researcher at the University of Southern California, where he worked on large-scale and distributed machine learning with applications to automatic speech recognition. He is currently a postdoctoral researcher at Telecom ParisTech (France), working on machine learning for big data. Contributor Bio:  Habrard, Amaury Aurelien Bellet received his Ph. D. in Machine Learning from the University of Saint-Etienne (France) in 2012. His work focused on algorithmic and theoretical aspects of metric and similarity learning. After completing his thesis, he was a postdoctoral researcher at the University of Southern California, where he worked on large-scale and distributed machine learning with applications to automatic speech recognition. He is currently a postdoctoral researcher at Telecom ParisTech (France), working on machine learning for big data. Contributor Bio:  Sebban, Marc Aurelien Bellet received his Ph. D. in Machine Learning from the University of Saint-Etienne (France) in 2012. His work focused on algorithmic and theoretical aspects of metric and similarity learning. After completing his thesis, he was a postdoctoral researcher at the University of Southern California, where he worked on large-scale and distributed machine learning with applications to automatic speech recognition. He is currently a postdoctoral researcher at Telecom ParisTech (France), working on machine learning for big data.