Multiblock Data Fusion in Statistics and Machine Learning. Tormod Næs
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Foreword
It is a real honour to write a few introductory words about Multiblock Data Fusion in Statistics and Machine Learning. The book is maybe not timely! The subject has been around in chemometrics since the late 1980s; usually under the term multiblock analysis.
Let me take that back immediately–the book is definitely timely. Even though this subject has been discussed for decades, it has taken off dramatically lately. And not only in chemometrics, but in a variety of fields. There are many diverse and interesting developments and in fact, it is quite difficult to really understand what is going on and to filter or even just understand the literature from so many sources. Each field will have their own internal jargon and background. This may be the biggest obstacle right now. It is evident that there are many interesting developments but grasping them is next to impossible. This book fixes that. And not only that, this book provides a comprehensive overview across fields and it also adds perspective and new research where needed. I would argue that this is the place if you want to understand data fusion comprehensively.
That is, if you want to understand how to apply data fusion; or you want to develop new data fusion models; or learn how the algorithms and models work; or maybe you want to understand what the shortcomings of different approaches are. If you have questions like these or you simply want to know what is happening in this area of data science, then reading this book will be a nice and fulfilling experience.
To write a comprehensive book about such an enormous field requires special people. And indeed, there are three very competent persons behind this book. They have all worked within the