Detecting manifold dependences of multivariate data with total correlation
Intelligent Data Analysis, ISSN: 1571-4128, Vol: 22, Issue: 3, Page: 467-489
2018
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Article Description
Discovering dependences between variables has a significant impact on the performance of exploration on large datasets. Many useful measures have been presented to identify interesting dependences for pairs of variables, but few for triplets. Here, we proposed a novel measure of dependence for three-variable relationships: the maximal total correlation coefficient (MTCC). With a score roughly equaling the determination coefficient R2, MTCC captures a wide range of trivariate one-dimensional manifold dependences, including many common space curves. Applying MTCC to datasets in global health and major-league baseball, we identify a number of almost unknown manifold dependences, especially an impressive superposition of three trivariate relationships.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85047322502&origin=inward; http://dx.doi.org/10.3233/ida-163324; https://journals.sagepub.com/doi/full/10.3233/IDA-163324; https://dx.doi.org/10.3233/ida-163324; https://content.iospress.com:443/articles/intelligent-data-analysis/ida163324
SAGE Publications
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