Independent Component Analysis (ICA) has found a wide range of applications in signal processing and multimedia, ranging from speech cleaning to face recognition. This paper presents a non-parametric approach to the ICA problem that is robust towards outlier effects. The algorithm, for the first time in the field of ICA, adopts an intuitive and direct approach, focusing on the very definition o...

Finding cluster head (CH) is an important issue in WSN. A new optimization algorithm Imperialist Competitive Algorithm (ICA) has been introduced recently, inspired by socio-political process of imperialistic competition. We use ICA for CH selection according to the communication energy (CE) cost. We demonstrate that ICA is an effective method for selection of CH in WSN. ICA either finds one or ...

In graph theory, Graph Colouring Problem (GCP) is an assignment of colours to vertices of any given graph such that the colours on adjacent vertices are different. The GCP is known to be an optimization and NP-hard problem. Imperialist Competitive Algorithm (ICA) is a meta-heuristic optimization and stochastic search strategy which is inspired from socio-political phenomenon of imperialistic co...

Guided by the principles of geometric independent component analysis (ICA), we present a new approach (SOMICA) to linear geometric ICA using a self-organizing map (SOM). We observe a considerable improvement in separation quality of different distributions, albeit at high computational costs. The SOMICA algorithm is therefore primarily interesting from a theoretical point of view bringing toget...

Permutation ambiguity of the classical Independent Component Analysis (ICA) may cause problems in feature extraction for pattern classification. Especially when only a small subset of components is derived from data, these components may not be most distinctive for classification, because ICA is an unsupervised method. We include a selective prior for de-mixing coefficients into the classical I...

in this paper, a two-surfaces sliding mode controller (tssmc) is proposed for the voltage tracking control of a two input dc-dc converter in application of electric vehicles (evs). the imperialist competitive algorithm (ica) is used for tuning tssmc parameters. the proposed controller significantly improves the transient response and disturbance rejection of the two input converters while prese...

It is well known that principal component analysis (PCA) only considers the second-order statistics and that independent component analysis (ICA) exploits higher-order statistics of the data. In this paper, for whitened data, we give an elegant way to incorporate higherorder statistics implicitly in the form of second-order moments, and show that ICA can be performed by PCA following a simple t...

Abstract The detection of sensor faults has proven to be easier through data-driven methods which rely on historical data collected from sensors that are placed at various locations in a process plant. Since the distribution industrial variables is random and non-Gaussian, independent component analysis (ICA) method been better suited for fault (FD) problems. Whenever comes with any level noise...

We propose a new method for training iterative collective classifiers for labeling nodes in network data. The iterative classification algorithm (ICA) is a canonical method for incorporating relational information into the classification process. Yet, existing methods for training ICA models rely on computing relational features using the true labels of the nodes. This method introduces a bias ...

Independent component analysis (ICA) is a family of unsupervised learning algorithms that have proven useful for the analysis of the electroencephalogram (EEG) and magnetoencephalogram (MEG). ICA decomposes an EEG/MEG data set into a basis of maximally temporally independent components (ICs) that are learned from the data. As with any statistic, a concern with using ICA is the degree to which t...