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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 Face Recognition Matlab code - YouTube
A number of face recognition algorithms employ principal component analysis (PCA), which is based on the second-order statistics of the image set, and does not address high-order statistical ...
ICA for dummies – Arnaud Delorme
Now, if one want to remove component number 2 from the data (for instance if component number 2 proved to be an artifact), one can simply subtract the matrix above (XC2) from the original data X. Note that in the matrix computed above (XC2) all the columns are proportional, which mean that the scalp activity is simply scaled. For this reason, we denote the columns of the W-1 matrix, the scalp ...
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.
Independent Component Analysis - an overview ...
jICA is an extension of the popular independent component analysis (ICA). Briefly, ICA is a technique for revealing hidden factors that underlie a set of observable data. ICA has been widely used to solve blind source separation problems (Fig. 16.3); these include, for example, the problem of deriving brain waves recorded using multiple sensors and the problem of removing interfering radio ...
Research - M. Alex O. Vasilescu
In the context of facial image ensembles, we demonstrate that the statistical regularities learned by MICA capture information that improves automatic face recognition. "Multilinear (Tensor) ICA and Dimensionality Reduction", M.A.O. Vasilescu, D. Terzopoulos, Proc. 7th International Conference on Independent Component Analysis and Signal ......
Action recognition based on overcomplete independent ...
Motivated by two observations: (1) independent component analysis (ICA) is capable of encoding intrinsic features underlying video data; and (2) videos of different actions can be easily distinguished by their intrinsic features, we propose a simple but effective action recognition framework based on the recently proposed overcomplete ICA model.
ICA on Images with Python - Open Source Automation
Also, by its nature, ICA extracts the independent components of images — which means that it will find the curves and edges within an image. For example, in facial recognition, ICA will identify the eyes, the nose, the mouth etc. as independent components. ICA can be implemented in several open source languages, including Python, R, and Scala....
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.
Learning Modewise Independent Components from Tensor Data ...
Independent component analysis (ICA) is a popular unsupervised learning method. This paper extends it to multilinear modewise ICA (MMICA) for tensors and explores two architectures in learning and recognition. MMICA models tensor data as mixtures generated from modewise source matrices that encode statistically independent information....
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