Independent component analysis: recent advances . . . Independent component analysis (ICA; Jutten Hérault [1]) has been established as a fundamental way of analysing such multi-variate data It learns a linear decomposition (transform) of the data, such as the more classical methods of factor analysis and principal component analysis (PCA)
Advances in Independent Component Analysis | SpringerLink Independent Component Analysis (ICA) is a fast developing area of intense research interest Following on from Self-Organising Neural Networks: Independent Component Analysis and Blind Signal Separation, this book reviews the significant developments of the past year
Independent Component Analysis - Blind Source Separation . . . The origin and development of the ICA algorithm (independent component analysis) and its application in various fields are introduced The basic principles of the ICA algorithm and the main problems in ICA are explained and described
[2106. 05200] Independent mechanism analysis, a new concept? We investigate an alternative path and consider instead including assumptions reflecting the principle of independent causal mechanisms exploited in the field of causality Specifically, our approach is motivated by thinking of each source as independently influencing the mixing process
Independent Component Analysis: an Introduction - ResearchGate Independent component analysis (ICA) is a widely-used blind source separation technique ICA has been applied to many applications ICA is usually utilized as a black box, without understanding
Independent mechanism analysis, a new concept? - NeurIPS Independent component analysis provides a principled framework for unsupervised representation learning, with solid theory on the identifiability of the latent code that generated the data, given only observations of mixtures thereof
Independent Component Analysis: Theory and Applications . . . Independent Component Analysis: Theory and Applications is the first book to successfully address this fairly new and generally applicable method of blind source separation It is essential reading for researchers and practitioners with an interest in ICA