App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 5

Multi-view unsupervised feature selection guided by latent representation and tensor learning

Jiang Jianjun
Xie Xijiong
School of Information Science and Engineering, Ningbo University, Ningbo Zhejiang 315211, China
DOI: 10.19734/j.issn.1001-3695.2025.09.0394

Abstract

To address the issues in multi-view unsupervised feature selection where directly constructing graphs from raw noisy features often ignores global structures and conventional multi-graph fusion methods struggle to capture consistent shared information due to view heterogeneity, this study proposes a feature selection model integrating latent representation and tensor learning. The method first maps raw multi-view data into a common latent space to capture high-level semantic structures and conducts subspace learning to remove irrelevant features. It further employs spectral embedding to preserve shared local geometry and utilizes a low-rank tensor constraint to model high-order interactions across views, while incorporating an -norm regularization into the feature regression framework to enhance robustness. By leveraging latent representation learning and tensor-based high-order correlation modeling, the method can more effectively capture consistent structures and intrinsic discriminative information in multi-view data, thereby improving the stability and representativeness of feature selection. Extensive experiments on six benchmark datasets demonstrate that the proposed method significantly outperforms state-of-the-art approaches across multiple evaluation metrics.

Foundation Support

自然科学基金资助项目(61906101)
浙江省自然科学基金资助项目(LQ18F020001)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.09.0394
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 5

Publish History

[2026-01-20] Accepted Paper

Cite This Article

蒋剑军, 谢锡炯. 基于潜在表示和张量学习的多视图无监督特征选择方法 [J]. 计算机应用研究, 2026, 43 (5). (2026-01-20). https://doi.org/10.19734/j.issn.1001-3695.2025.09.0394. (Jiang Jianjun, Xie Xijiong. Multi-view unsupervised feature selection guided by latent representation and tensor learning [J]. Application Research of Computers, 2026, 43 (5). (2026-01-20). https://doi.org/10.19734/j.issn.1001-3695.2025.09.0394. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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