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Spectral Self-supervised Feature Selection
Daniel Segal
,
Ofir Lindenbaum
, Ariel Jaffe
Bar-Ilan University - The Alexander Kofkin Faculty of Engineering
Hebrew University of Jerusalem
Research output
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Contribution to journal
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peer-review
Overview
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Dive into the research topics of 'Spectral Self-supervised Feature Selection'. Together they form a unique fingerprint.
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Computer Science
Feature Selection
100%
Eigenvector
100%
Feature Extraction
100%
Dimensionality Reduction
50%
Challenging Scenario
50%
Multiple Domain
50%
Laplace Operator
50%
Processing Step
50%
Keyphrases
Supervised Feature Selection
100%
Graph Laplacian Eigenvectors
33%