Average Reviews:
(More customer reviews)I wish that more math books were written this way. Roberts and Everson give an excellent treatment of this technique, and illustrate some of its most useful variations. It's written in a very academic style and is suited for any graduate student or an undergraduate with familiarity in machine learning. Good linear algebra skills are also recommended. Small and light enough to hold on your lap or read in bed, but carrying all the depth that you'd expect from the seminal papers that pioneered the work. An enjoyable read.
Click Here to see more reviews about: Independent Component Analysis: Principles and Practice
Independent Components Analysis (ICA) is an important tool for modeling and understanding empirical data sets. Belonging to the class of general linear models, it is a method of separating out independent sources from linearly mixed data. 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 and includes an extensive introduction to ICA. It reviews the major theoretical bases from a modern perspective, surveys current developments, and describes many case studies of applications in detail. Applications include biomedical examples, signal and image denoising, and mobile communications. The book discusses ICA within the framework of general linear models, but it also compares it to other paradigms such as neural network and graphical modeling methods.
Click here for more information about Independent Component Analysis: Principles and Practice
No comments:
Post a Comment