Independent Component Analysis based on multiple data-weighting
This is an incremental improvement for data analysis in fields like signal processing, offering a new method for ICA.
The paper tackles the problem of independent component analysis by introducing the MWeICA algorithm, which uses weighted covariance matrices to achieve independence, and reports that it outperforms most state-of-the-art methods with similar computational time.
Independent Component Analysis (ICA) - one of the basic tools in data analysis - aims to find a coordinate system in which the components of the data are independent. In this paper we present Multiple-weighted Independent Component Analysis (MWeICA) algorithm, a new ICA method which is based on approximate diagonalization of weighted covariance matrices. Our idea is based on theoretical result, which says that linear independence of weighted data (for gaussian weights) guarantees independence. Experiments show that MWeICA achieves better results to most state-of-the-art ICA methods, with similar computational time.