Abstract

COMPARISON STUDY BETWEEN INDEPENDENT COMPONENT ANALYSIS AND PRINCIPLE COMPONENT ANALYSIS IN THE CONTEXT OF HIDDEN SOURCE SEPARATION

Md. Shohel Rana*, Md. Mahabubur Rahman and Md. Sabbir Hasnain

Abstract

The report presents comparative assessment of Blind Source Separation methods for instantaneous mixtures. The study highlights the underlying principles of Principal Component Analysis (PCA) and Independent Component Analysis (ICA) in this context. These methods have been tested on instantaneous mixtures of simultaneous data, monotonous and secondary data. In particular, methods based on PCA and Fast ICA, have been compared for their separation ability, processing time and accuracy. The quality of the output, the complexity of the algorithms and the simplicity (implementation) of the methods are some of the performance measures which are highlighted with respect to the above source data.

Keywords: Principle component analysis (PCA), Independent component analysis (ICA), Signal to Interference Ratio (SIR), Blind source separation (BSS), source of interest (SOI), Source Separation (SS).