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
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Part 1: Methods 1. The Initial Convergence Rate of the FastICA Algorithm: The "One-Third Rule" 2. Improved variants of the FastICA algorithm 3. A unified probabilistic model for independent and principal component analysis 4. Riemannian optimization in complex-valued ICA 5. Non-Additive Optimization 6. Image denoising via local factor analysis under Bayesian Ying-Yang principle 7. Unsupervised Deep Learning: A Short Review 8. From Neural PCA to Deep Unsupervised Learning
Part 2: Applications 9. Two Decades of Local Binary Patterns
A Survey 10. Subspace approach in Spectral Color Science 11. From pattern recognition methods to machine vision applications 12. Advances in Visual Concept Detection: Ten Years of TRECVID 13. On the applicability of latent variable modeling to research system data
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.