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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 algorithmUnsupervised deep learning Machine vision and image retrieval A review of developments in the t

Independent component analysis. --- Machine learning.

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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.

Neural networks (Computer science) --- Independent component analysis.

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This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact

Science: general issues --- Diversity orientated synthesis --- Multicomponent reactions (MCRs) --- Fragment based drug design --- Chemoimformatics --- principal component analysis—PCA --- Heterocycles --- Diversity orientated synthesis --- Multicomponent reactions (MCRs) --- Fragment based drug design --- Chemoimformatics --- principal component analysis—PCA --- Heterocycles

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The main objective of this book is to analyse and detect small changes in ECG waves and complexes that indicate cardiac diseases and disorders. Detecting predisposition to Torsade de Points (TDP) by analysing the beat-to-beat variability in T wave morphology is the main core of this work. The second main topic is detecting small changes in QRS complex and predicting future QRS complexes of patients. Moreover, the last main topic is clustering similar ECG components in different groups.

Wavelet --- TdP --- Baseline Wander --- ECG --- Torsade de Points --- Feature Extraction --- Biosignal Pr --- Multi-Channel ECG --- Principal Component Analysis --- PCA

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Mathematical statistics --- Multivariate analysis --- Principal components analysis --- Analyse multivariée --- Analyse en composantes principales --- 519.6 --- Computational mathematics. Numerical analysis. Computer programming --- Independent component analysis. --- Multivariate analysis. --- Principal components analysis. --- Basic Sciences. Statistics --- Multivariate Statistics --- Multivariate Statistics. --- 519.6 Computational mathematics. Numerical analysis. Computer programming --- Analyse multivariée --- Independent component analysis --- ICA (Independent component analysis) --- Theorie du signal