Dependent component analysis

Dependent Component Analysis (DCA) is a statistical technique used to analyze data consisting of multiple variables that may be dependent on each other. Unlike Independent Component Analysis (ICA), which seeks to decompose a multivariate signal into statistically independent components, DCA focuses on identifying and modeling relationships among components that exhibit correlation or dependencies. ### Key Features of Dependent Component Analysis: 1. **Modeling Dependencies**: DCA is designed to model and analyze the joint distribution of multiple variables where dependencies exist.