Advances in Independent Component Analysis and Learning Machines - Hardcover

Ella Bingham

Hardcover

ISBN 10: 0128028068 ISBN 13: 9780128028063

Publisher: Academic Press Inc, 2015

Synopsis

In honour of Professor Erkki Oja, one of the pioneers of Independent Component Analysis (ICA), this book reviews key advances in the theory and application of ICA, as well as its influence on signal processing, pattern recognition, machine learning, and data mining. Examples of topics which have developed from the advances of ICA, which are covered in the book are: * A unifying probabilistic model for PCA and ICA * Optimization methods for matrix decompositions * Insights into the FastICA algorithm* Unsupervised deep learning * Machine vision and image retrieval * A review of developments in the theory and applications of independent component analysis, and its influence in important areas such as statistical signal processing, pattern recognition and deep learning.* A diverse set of application fields, ranging from machine vision to science policy data.* Contributions from leading researchers in the field.

About this title

About the Author

Ella Bingham received her Doctor of Science (PhD) degree in Computer Science in 2003, and MSc degree in Systems and Operations Research in 1998, both at Helsinki University of Technology. Her main research field has been statistical data analysis. She works at Helsinki Institute for Information Technology HIIT at Aalto University and University of Helsinki. In addition, she is Executive Director of the Foundation for Aalto University Science and Technology. Her professional interests include science policy, research administration, research assessments, and research funding. Samuel Kaski received the DSc (PhD) degree in Computer Science from Helsinki University of Technology, Finland, in 1997. He is currently a Professor at Aalto University, the Director of Helsinki Institute for Information Technology HIIT, Aalto University and University of Helsinki, Finland, and the Director of Finnish Centre of Excellence in Computational Inference Research COIN. He is an action editor of the Journal of Machine Learning Research, and has chaired several conferences including AISTATS 2014. He has published over 200 peer-reviewed papers and supervised 18 PhD theses. His current research interests include statistical machine learning, computational biology and medicine, information visualization, and exploratory information retrieval. Jorma Laaksonen has worked with Prof. Erkki Oja since 1994 and got his Dr. of Science in Technology degree in 1997 from Helsinki University of Technology, Finland. Presently he is a permanent teaching research scientist at the Department of Information and Computer Science, Aalto School of Science where he has instructed eight doctoral theses in the supervision of Prof. Oja. He is an author of 200 scientific journal, conference and edited book papers on pattern recognition, statistical classification, machine learning and neural networks, with Google Scholar h-index 27. His research interests are in content-based multimodal information retrieval and computer vision. Dr. Laaksonen is an Associate Editor of Pattern Recognition Letters, IEEE senior member, and a founding member of the SOM and LVQ Programming Teams and the PicSOM Development Group. Jouko Lampinen obtained his DSc (PhD) degree in Information Technology from Lappeenranta University of Technology, in 1993. He is currently a Professor at Aalto University, Department of Biomedical Engineering and Computational Science, and Vice Dean of School of Science. He is the director of Aalto MSc programme in Life Science Technologies. He has published over 100 peer-reviewed papers and supervised or co-supervised over 20 PhD theses. His current research interests include probabilistic modeling, and data-analysis in systemic neuroscience.

From the Back Cover

In honour of Professor Erkki Oja, one of the pioneers of Independent Component Analysis (ICA), this book reviews key advances in the theory and application of ICA, as well as its influence on signal processing, pattern recognition, machine learning, and data mining.

Examples of topics which have developed from the advances of ICA, which are covered in the book are:

• A unifying probabilistic model for PCA and ICA
• Optimization methods for matrix decompositions
• Insights into the FastICA algorithm
• Unsupervised deep learning
• Machine vision and image retrieval

Key Features Include:

• A review of developments in the theory and applications of independent component analysis, and its influence in important areas such as statistical signal processing, pattern recognition and deep learning.
• A diverse set of application fields, ranging from machine vision to science policy data.
• Contributions from leading researchers in the field

This book is ideal for researchers who want to understand how the theory of ICA has developed and been applied to important areas such as machine learning, pattern recognition, signal processing and data mining.

Edition details

• Publisher: Academic Press Inc
• Publication date: 2015
• Language: English
• ISBN 10: 0128028068
• ISBN 13: 9780128028063
• Binding: Hardcover
• Number of pages: 328
• Editor: Ella Bingham, Samuel Kaski, Jorma Laaksonen, Jouko Lampinen