|
Web
Education
Viewing 1-4 of 4 total results
Independent component analysis - Wikipedia
In signal processing, independent component analysis (ICA) is a computational method for separating a multivariate signal into additive subcomponents. This is done by assuming that the subcomponents are non-Gaussian signals and that they are statistically independent from each other. ICA is a special case of blind source separation.A common example application is the "cocktail party problem ......
ICA for dummies – Arnaud Delorme
Independent Component Analysis for dummies Introduction. Independent Component Analysis is a signal processing method to separate independent sources linearly mixed in several sensors. For instance, when recording electroencephalograms (EEG) on the scalp, ICA can separate out artifacts embedded in the data (since they are usually independent of ......
Financial time series forecasting using independent ...
An example is used for illustrating the concept of the TnA method.Fig. 2 shows four financial time series data, each of size 1 × 794, which can be combined as a mixture matrix X of size 4 × 794. After using ICA method to the matrix X, a de-mixing matrix W of size 4 × 4 and four ICs, each of size 1 × 794, can be estimated. The profiles of those four ICs are shown in Fig. 3.
 Independent Component Analysis: Algorithms and Applications
The statistical model in Eq. 4 is called independent component analysis, or ICA model. The ICA model is a generative model, which means that it describes how the observed data are generated by a process of mixing the components si. The independent components are latent variables, meaning that they cannot be directly observed....
|
