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Face recognition using independent component analysis and ...
Independent component analysis (ICA) (Bell and Sejnowski, 1995) is also a relatively recent technique which has been mainly applied to blind signal separation, though it has been successfully applied to the face recognition problem too. ICA is a feature extraction technique, while SVM are a type of classifiers.
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 ......
Independent components analysis-based nose detection method
The method adopt Independent Components Analysis (ICA) as a subspace classifier to classify the face candidate region to nose or non nose. The ICA basis vectors are estimated by the FastICA algorithm. The training has been done using features of nose appearance and shape characterized by the edge information.
 RESEARCH ARTICLE An Investigation of Face Recognition ...
This aims to investigate the face recognition characteristics using widely adopted statistical approaches (i.e., Principal Component Analysis (PCA) and Independent Component Analysis (ICA)). This paper focus on Eigen faces approach for implementing the face recognition and detection on the images to compare the performance of PCA and ICA....
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.
Multilinear independent components analysis - IEEE ...
Abstract: Independent components analysis (ICA) maximizes the statistical independence of the representational components of a training image ensemble, but it cannot distinguish between the different factors, or modes, inherent to image formation, including scene structure, illumination, and imaging. We introduce a nonlinear, multifactor model that generalizes ICA.
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....
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