Demo corpus. Scores are computed on a select set of biomedical paper/datasets and may be inaccurate for papers outside this corpus — DataRank relies on network effects that improve with scale. We aim to expand this into a fully open resource pending additional funding.

On the use of joint diagonalization in blind signal processing

(2006)10.1109/iscas.2006.1693402Source: DataRank Database

On the use of joint diagonalization in blind signal processing is a research paper (2006). On theSindex it has a DataRank of 0.500. It has been cited 27 times.

N/A

0.500DataRank · unranked

0.500

27 citations · base score 3.3

Cite:

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Abstract

Blind source separation (BSS) tries to decompose a given multivariate data set into the product of a mixing matrix and a source vector, both of which are unknown. The sources can be recovered if we pose additional constraints to this model. One class of BSS algorithms is given by algebraic BSS, which recovers the mixing structure by jointly diagonalizing various source condition matrices corresponding to different source models. We review classical BSS algorithms such as FOBI, JADE, AMUSE, SOBI, TDSEP and SONS within this framework; combination of the respective source conditions can then yield additional algorithms as implemented e.g. by JADE TD . Extensions to dependent component analysis models such as spatiotemporal or multidimensional BSS are discussed

›Data sources & pipeline

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Pending

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (0/2)

Interoperable (0/2)

Reusable (0/3)

FAIR checklist signals are shown for context only and do not affect DataRank scoring.

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DataRank Breakdown

Base Score 100%Citation Network 0%

Base Score Contribution

0.500

From this paper's citation signal

Citation Network Contribution

Citation network not refreshed for this result

This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.

Learn more about DataRank methodology →

Why this DataRank?

DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.

Base score B(p): log1p(citation_count) — grows sub-linearly, so a paper with 1,000 citations is not 10× a paper with 100.
Network N(p): Σ over citers of log1p(C_q) ÷ max(outdegree_q, 1). Being cited by a highly-cited paper with few references counts most.
Damping factor d = 0.85: DataRank = (1−d)·B(p) + d·N(p) — the two cards above are each already multiplied by their share.
Self-citations excluded: Citers sharing any OpenAlex author ID with this paper are filtered out before the network sum.

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.

Read the full methodology →