Performance Evaluation of Independent Component Analysis-Based Fault Detection Using Measurements Corrupted with Noise

Noise-resistant joint diagonalization independent component analysis based process fault detection

Fast independent component analysis (FastICA) is an efficient feature extraction tool widely used for process fault detection. However, the conventional FastICA-based fault detection method does not consider the ubiquitous measurement noise and may exhibit unsatisfactory performance under the adverse effects of the measurement noise. To solve this problem, we propose a new process fault detecti...

​Rank based Least-squares Independent Component Analysis

  In this paper, we propose a nonparametric rank-based alternative to the least-squares independent component analysis algorithm developed. The basic idea is to estimate the squared-loss mutual information, which used as the objective function of the algorithm, based on its copula density version. Therefore, no marginal densities have to be estimated. We provide empirical evaluation of th...

Selective Noise Cancellation Using Independent Component Analysis

Automatic Detection of Filters in Images with Gaussian Noise Using Independent Component Analysis

In this article we present the results of a study carried out using the popular fastica algorithm applied to the detection of filters in natural images in gray-scale, contaminated with gaussian noise. The detection of filters has been accomplished by using the statistical distribution measures kurtosis and skewness.

Multivariate Outlier Detection Using Independent Component Analysis

The recent developments by considering a rather unexpected application of the theory of Independent component analysis (ICA) found in outlier detection , data clustering and multivariate data visualization etc . Accurate identification of outliers plays an important role in statistical analysis. If classical statistical models are blindly applied to data containing outliers, the results can be ...