Comparison of Clustering Methods for Extraction of Uncorrelated Sparse Sources from Data Mixtures

TL;DR

This paper introduces a novel clustering-based method for extracting uncorrelated sparse sources from data mixtures, demonstrating comparable performance to existing algorithms across various source types and real fetal ECG data.

Contribution

A new clustering approach for source separation that estimates sources within a multiplicative constant, compared with existing methods and FastICA.

Findings

Comparable performance to existing methods on sparse and non-sparse sources

Effective on fetal ECG data

Estimates sources within a multiplicative constant

Abstract

There is an extensive set of methods to determine sparse sources from mixtures where the mixing coefficients are unknown. Each method involves plotting N sets of mixed data against each other in N-dimensional space. In the approach adopted in this paper, N dimensional normalised vectors are produced by joining data points that are adjacent in time. A novel clustering approach is adopted: the two vectors, not necessarily adjacent in time, which are closest to each other are identified and one of these vectors is taken as the principal direction corresponding to one of the sources. It is shown, using a deflation approach, that it is possible to estimate individual sources to within a multiplicative constant. This novel method is compared with two related methods and the standard FastICA algorithm. This new method has comparable performances to three other methods when applied to examples of purely sparse, semi-sparse and non-sparse sources and also when applied to fetal ECG data.