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Independent Component Analysis
Independent Component Analysis (ICA) has recently become an important tool for modelling and understanding empirical datasets. It is a method of separating out independent sources from linearly mixed data, and belongs to the class of general linear models. ICA provides a better decomposition than other well-known models such as principal component analysis. This self-contained book contains a structured series of edited papers by leading researchers in the field, including an extensive introduction to ICA. The major theoretical bases are reviewed from a modern perspective, current developments are surveyed and many case studies of applications are described in detail. The latter include biomedical examples, signal and image denoising and mobile communications. ICA is discussed in the framework of general linear models, but also in comparison with other paradigms such as neural network and graphical modelling methods. The book is ideal for researchers and graduate students in the field.
The House That Hitler Built
This is a new release of the original 1938 edition.
Current Catalog
First multi-year cumulation covers six years: 1965-70.
Health Planning Reports Personal Author Index
Lists citations to the National Health Planning Information Center's collection of health planning literature, government reports, and studies from May 1975 to January 1980.
How to Follow Your Instincts on the Stock Market
What do Greta Thunberg, Elon Musk and Warren Buffett have in common? As far apart as they may seem, these global icons represent key themes in Stephen Roberts' time-tested approach to picking stocks with such common sense that teenagers are now using it to build their future after COVID-19.
The Statutes at Large of the United States
Statutes at Large is the official annual compilation of public and private laws printed by the GPO. Laws are arranged by order of passage.
