Review and Analysis of Independent Component for Efficient Algorithm

Abstract

This paper considers the identification of latent structure in massive   data . It is consider that unknown latent data or variables and relation among them  are origin of Latent structure. This is very important to identifying these kinds of interrelated latent variables form the given data source. It is very important to  identifying the these latent variable along with  its complex connectivity, its dependencies from the  bulk of data given ,because these latent variable are increases the unused   computation without its use. Independent component analysis  is one of the popular method that use for this identifying. This method is use for the expressing the multidimensional connecting among the data , its proper observation and identification of unknown latent data and  its dependencies for other  data , either it is valuable or not . This paper include the some of the existing technique that are able deal the same problem mention along with the ICA scheme a way of  involving the ICA is introduce and discussed here in detail and different types of ICA algorithm is analyzed in this paper for the different data. This is difficult task filtering the latent data and its complex  structure, which type of ICA algorithm is suitable for filtration of latent variable form the given massive data and produces the best result.