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There is an extensive set of methods to determine sparse sources frommixtures where the mixing coefficients are unknown. Each method involvesplotting N sets of mixed data against each other in N-dimensional space. In theapproach adopted in this paper, N dimensional normalised vectors are producedby joining data points that are adjacent in time. A novel clustering approachis adopted: the two vectors, not necessarily adjacent in time, which areclosest to each other are identified and one of these vectors is taken as theprincipal direction corresponding to one of the sources. It is shown, using adeflation approach, that it is possible to estimate individual sources towithin a multiplicative constant. This novel method is compared with tworelated methods and the standard FastICA algorithm. This new method hascomparable performances to three other methods when applied to examples ofpurely sparse, semi-sparse and non-sparse sources and also when applied tofetal ECG data.

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