Evaluating and selecting features via information theoretic lower bounds of feature inner correlations for high-dimensional data

Research article (European Journal of Operational Research, 2020) · cited 53× · AI/ML
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Evaluating and selecting features via information theoretic lower bounds of feature inner correlations for high-dimensional data

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Evaluating and selecting features via information theoretic lower bounds of feature inner correlations for high-dimensional data is a scholarly article[1].

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APA 4ort.xyz Knowledge Graph. (2026). Evaluating and selecting features via information theoretic lower bounds of feature inner correlations for high-dimensional data. Retrieved May 24, 2026, from https://4ort.xyz/entity/evaluating-and-selecting-features-via-information-theoretic-lower-bounds-of-feature-inner-correlations-for-high-dimensio
MLA “Evaluating and selecting features via information theoretic lower bounds of feature inner correlations for high-dimensional data.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/evaluating-and-selecting-features-via-information-theoretic-lower-bounds-of-feature-inner-correlations-for-high-dimensio.
BibTeX @misc{4ortxyz_evaluating-and-selecting-features-via-information-theoretic-lower-bounds-of-feature-inner-correlations-for-high-dimensio_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Evaluating and selecting features via information theoretic lower bounds of feature inner correlations for high-dimensional data}}, year = {2026}, url = {https://4ort.xyz/entity/evaluating-and-selecting-features-via-information-theoretic-lower-bounds-of-feature-inner-correlations-for-high-dimensio}, note = {Accessed: 2026-05-24}}
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