GOLFS: feature selection via combining both global and local information for high dimensional clustering

Zhaoyu Xing, Yang Wan, Juan Wen () and Wei Zhong ()
Additional contact information
Zhaoyu Xing: Xiamen University
Yang Wan: ByteDance Ltd.
Juan Wen: Xiamen University
Wei Zhong: Xiamen University

Computational Statistics, 2024, vol. 39, issue 5, No 10, 2675 pages

Abstract: Abstract It is important to identify the discriminative features for high dimensional clustering. However, due to the lack of cluster labels, the regularization methods developed for supervised feature selection can not be directly applied. To learn the pseudo labels and select the discriminative features simultaneously, we propose a new unsupervised feature selection method, named GlObal and Local information combined Feature Selection (GOLFS), for high dimensional clustering problems. The GOLFS algorithm combines both local geometric structure via manifold learning and global correlation structure of samples via regularized self-representation to select the discriminative features. The combination improves the accuracy of both feature selection and clustering by exploiting more comprehensive information. In addition, an iterative algorithm is proposed to solve the optimization problem and the convergency is proved. Simulations and two real data applications demonstrate the excellent finite-sample performance of GOLFS on both feature selection and clustering.

Keywords: Feature selection; High dimensionality; $$l_{2{; }1}$$ l 2; 1 -norm; Manifold learning; Regularized self-representation; Spectral clustering (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s00180-023-01393-x

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