The use of component analysis on fMRI data is an important neuroimaging computational tool. In this paper we focus on the particular application of extracting the so-called default mode neuronal network from resting brain data ([1] and references therein). While independent component analysis (ICA) is currently the method of choice in this application, we investigate the advantages and limitati...

It is known that high-dimensional human motion data lies in a low dimensional space. Looking for independent sub-motions that contribute to generating complicated human motions is an interesting task on its own merits. Independent Component Analysis (ICA) can extract independent factors of an observed signal. In this paper, ICA is applied to the reconstructed human motion data by PCA. Experimen...

One main drawback of intrusion detection system is the inability of detecting new attacks which do not have known signatures. In this paper we discuss an intrusion detection method that proposes independent component analysis (ICA) based feature selection heuristics and using rough fuzzy for clustering data. ICA is to separate these independent components (ICs) from the monitored variables. Rou...

A method for discrimination and classification of breast cancer dataset with benign and malignant tissues is proposed using Independent Component Analysis (ICA) and Self Organizing Map (SOM). The method implement ICA for preprocessing and data reduction and SOM for data analysis. The best performance was obtained with ICASOM, resulting in 98.8% classification accuracy and a SOM result is 94.9%.

Chemometric methods like Principal Component Analysis (PCA) and Evolving Factor Analysis (EFA) have been applied to improve peaks separation, especially in HPLC UV-DAD analysis. In this work, the Independent Component Analysis (ICA) was adopted for the separation of overlapping aromatic peaks and the simultaneous determination of the underlying spectra. The application of the method on middle p...

Support Vector Machines (SVM) and Independent Component Analysis (ICA) are two powerful and relatively recent techniques. SVMs are classifiers which have demonstrated high generalization capabilities. ICA is a feature extraction technique which can be considered a generalization of Principal Component Analysis (PCA). In this paper we combine these two techniques for the face recognition problem...

Independent component analysis (ICA) has proved to be a highly useful tool for modeling brain data and in particular electroencephalographic (EEG) data. In this paper, a new method is presented that may better capture the underlying source dynamics than ICA algorithms hereto employed for brain signal analysis. We suppose that a brief, impulse-like activation of an effective signal source elicit...

We present computational holographic three-dimensional imaging and automated object recognition based on independent component analysis (ICA). Three-dimensional sensing of the scene is performed by computational holographic imaging of the objects using phase-shifting digital holography. We used principal components analysis to reduce data dimension and ICA to recognize the three-dimensional obj...

In this paper, it is shown that independent component analysis (ICA) of sparse signals (sparse ICA) can be seen as a cluster-wise principal component analysis (PCA). Consequently, Sparse ICA may be done by a combination of a clustering algorithm and PCA. For the clustering part, we use, in this paper, an algorithm inspired from K-means. The final algorithm is easy to implement for any number of...

Independent Component Analysis (ICA) of images of natural scenes has been shown to generate basis functions, or filters, which resemble spatial [Bell & Sejnowski (1997). Vision Research, 37, 3327-3338; van Hateren & van der Schaaf (1998). Proceedings of the Royal Society of London B, 265, 359-366] and spatiotemporal [van Hateren & Ruderman (1998) Proceedings of the Royal Society of London B, 26...