Median-based clustering for underdetermined blind signal processing is a research paper published in IEEE Signal Processing Letters (2006). On theSindex it has a DataRank of 1.6. It has been cited 29 times, with 28 citing works in its 1-hop citation network.
In underdetermined blind source separation, more sources are to be extracted from less observed mixtures without knowing both sources and mixing matrix. k-means-style clustering algorithms are commonly used to do this algorithmically given sufficiently sparse sources, but in any case other than deterministic sources, this lacks theoretical justification. After establishing that mean-based algorithms converge to wrong solutions in practice, we propose a median-based clustering scheme. Theoretical justification as well as algorithmic realizations (both online and batch) are given and illustrated by some examples.
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Base Score Contribution
0.510
From this paper's citation signal
Citation Network Contribution
1.0
From 19 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 33% comes from its base citations and 67% from the citation network (19 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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