Unsupervised Discriminative Feature Selection With $\ell _{2,0}$ℓ2,0-Norm Constrained Sparse Projection

Xia Dong; Feiping Nie; Lai Tian; Rong Wang; Xuelong Li

doi:10.1109/tpami.2025.3580669

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Unsupervised Discriminative Feature Selection With $\ell _{2,0}$ℓ2,0-Norm Constrained Sparse Projection

Xia Dong Feiping Nie Lai Tian Rong Wang Xuelong Li

Year: 2025 Journal: IEEE Transactions on Pattern Analysis and Machine Intelligence Vol: 47 (10)Pages: 8321-8335 Publisher: IEEE Computer Society

DOI: 10.1109/tpami.2025.3580669

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Abstract

Feature selection plays an important role in a wide spectrum of applications. Most of the sparsity-based feature selection methods tend to solve a relaxed $\ell _{2,p}$ℓ2,p-norm ($0 < p \leq 1$0

Keywords:

Discriminative model Feature selection Norm (philosophy) Artificial intelligence Cluster analysis Pattern recognition (psychology) Computer science Optimization problem Mathematics Feature (linguistics) Mathematical optimization

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Face and Expression Recognition

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Machine Learning and ELM

Physical Sciences → Computer Science → Artificial Intelligence

Advanced Computing and Algorithms

Social Sciences → Social Sciences → Urban Studies

Unsupervised Discriminative Feature Selection With $\ell _{2,0}$ℓ2,0-Norm Constrained Sparse Projection

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