Unsupervised feature selection by regularized self-representation

DOI10.1016/j.patcog.2014.08.006zbMath1373.68344OpenAlexW2083666679WikidataQ59533775 ScholiaQ59533775MaRDI QIDQ1677074

Qinghua Hu, Wangmeng Zuo, Lei Zhang, Peng-Fei Zhu, Simon Chi Keung Shiu

Publication date: 10 November 2017

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.patcog.2014.08.006

zbMATH Keywords

group sparsitysparse representationunsupervised feature selectionself-representation

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10)

Related Items (11)

A novel feature selection approach based on clustering algorithm ⋮ Hierarchical unsupervised multi-view feature selection ⋮ Locality preserving score for joint feature weights learning ⋮ Accelerated low-rank representation for subspace clustering and semi-supervised classification on large-scale data ⋮ Global and local structure preserving sparse subspace learning: an iterative approach to unsupervised feature selection ⋮ Unsupervised feature selection via adaptive autoencoder with redundancy control ⋮ Deep unsupervised feature selection by discarding nuisance and correlated features ⋮ Robust autoencoder feature selector for unsupervised feature selection ⋮ Label distribution learning by regularized sample self-representation ⋮ An efficient semi-supervised representatives feature selection algorithm based on information theory ⋮ Feature Selection Using Games with Imperfect Information (FSGIN)

Cites Work

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• Unsupervised feature selection using clustering ensembles and population based incremental learning algorithm
• Constraint score: A new filter method for feature selection with pairwise constraints
• 10.1162/153244303322753616
• A Review of Image Denoising Algorithms, with a New One

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