Dimension Reduction Techniques for Signal Separation Algorithms

While collecting data information, this received data, in most cases, are recorded with multiple number of variables, thus, this large dimension dataset will be so hard to visualize and then to be analysed for the purpose to be interpreted properly. The graphical representation may also not be helpful in case the dataset is too many. In this paper we will present a broad overview of two famous data reduction techniques known as the Principal Component Analysis and the Factorial Analysis. These two methods facilitate the interpretation of the data for the user, in a more meaningful form. Also this work highlights the big key differences existing between them and then, make easier the choice of using one of them according to different cases. In the context of ICA, this dimension reduction of the dataset represents a main first step for the famous problem known as Blind Source Separation (BSS).