This paper presents a new algorithm for the independent components analysis (ICA) problem based on efficient entropy estimates. Like many previous methods, this algorithm directly minimizes the measure of departure from independence according to the estimated Kullback-Leibler divergence between the joint distribution and the product of the marginal distributions. We pair this approach with effi...

FastICA is a kind of independent component analysis (ICA), which is robust and high performance algorithm, it can strongly remove signal correlation and ensure each signal to be independence. Through perceptual test, improving that RASTA is an idea which can denoise effectively. First, we remove signal correlation through FastICA algorithm, then we use RASTA filter to filtering the ceptral coef...

Hyperspectral image compression can be performed by either 3-D compression or spectral/spatial compression. It has been demonstrated that due to high spectral resolution hyperspectral image compression can be more effective if compression is carried out spectrally and spatially in two separate stages. One commonly used spectral/spatial compression implements principal components analysis (PCA) ...

Data-driven fMRI analysis techniques include independent component analysis (ICA) and different types of clustering in the temporal domain. Since each of these methods has its particular strengths, it is natural to look for an approach that unifies Kohonen's self-organizing map and ICA. This is given by the topographic independent component analysis. While achieved by a slight modification of t...

We analyse 3D human torso data using Principal Components Analysis (PCA) and Independent Components Analysis (ICA) and compare their respective principal and independent modes of variation. Both PCA and ICA have been used to analyse variations in observed data for different applications. PCA offers a means of capturing the significant variations present in a data sample while ICA is useful in i...

A recent paper by Daubechies et al. claims that two independent component analysis (ICA) algorithms, Infomax and FastICA, which are widely used for functional magnetic resonance imaging (fMRI) analysis, select for sparsity rather than independence. The argument was supported by a series of experiments on synthetic data. We show that these experiments fall short of proving this claim and that th...

Electromyography is a valuable tool in many clinical analyses, as it can give the clinician an accurate representation of what the muscles are doing to contribute to the desired task. For functional EMG the surface electrodes have the advantage of convenience and comfort. The major disadvantage to surface electrodes are cross talk and low level signal reception. Their adverse effects complicate...

In this paper, independent component analysis (ICA) is used for blind source separation of biomedical signals. Visual and quantitative tests of the ability of ICA to separate signals were performed using a fast ICA algorithm. Results obtained from simulated and FECG signals show that the ICA performance using the whitening matrix of the mixed signals was superior to that of random initial weights.

Abstract The author in order to solve the problem of optimizing accuracy mechanical equipment failure detection proposes a vibration signal collection and computer simulation for failure. Using wavelet domain Wiener filtering-based fault method, first combined filtering threshold filtering, established model equipment, obtained true filtered error fusion principle perform orthogonal transform o...

The purpose of this paper is to evaluate the results of various Independent Component Analysis (ICA) algorithms used for facial feature extraction. Face recognition algorithms results are mainly based on feature extractions from facial images. We have done various experimentations for facial feature extraction using ICA with global and local features from facial images. We have explored FastICA...