Deep unsupervised feature selection by discarding nuisance and correlated features

Deep unsupervised feature selection by discarding nuisance and correlated features

Modern datasets often contain large subsets of correlated features and nuisance features, which are not or loosely related to the main underlying structures of the data. Nuisance features can be identified using the Laplacian score criterion, which evaluates the importance of a given feature via its...

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Bibliographic Details
Main Authors: Kluger, Y. (Author), Lindenbaum, O. (Author), Shaham, U. (Author), Svirsky, J. (Author)
Format: Article
Language:English
Published: Elsevier Ltd 2022
Subjects:
article
Auto encoders
autoencoder
Clustering algorithms
Concrete layer
Concretes
Feature extraction
feature selection
Features sets
Given features
Graph laplacians
Laplace transforms
Laplacian score
Laplacians
Large dataset
Optimisations
Real-world datasets
Unsupervised feature selection
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