Zhang, PeipeiZhu, WenjieYan, Wei Qi2025-05-142025-05-142025IEEE Signal Processing Letters, ISSN: 1070-9908 (Print); 1558-2361 (Online), Institute of Electrical and Electronics Engineers (IEEE), 1-5. doi: 10.1109/lsp.2025.35694761070-99081558-2361http://hdl.handle.net/10292/19197Deep subspace clustering methods based on autoencoder (AE) have achieved impressive performance in various applications. However, these methods often place excessive reliance on the AE framework, which focuses primarily on pixel-level reconstruction while overlooking the structural information inherent in the data. To overcome this limitation, we propose a novel approach called the Multi-level Structural Contrastive Subspace Clustering Network (MSCSCN). Unlike traditional AE-based methods, MSCSCN departs from the AE paradigm and introduces multi-level contrastive prediction to improve feature learning. Specifically, MSCSCN integrates multi-level features from both original and augmented data within a self-expression learning process, enhancing the learned pairwise affinities. Additionally, we propose a structural contrastive loss, which strengthens cluster boundary discrimination by effectively utilizing pairwise affinities and structural information. Our experimental results on several benchmark datasets demonstrate that MSCSCN outperforms competitive deep subspace clustering methods, highlighting its superior capability in improving clustering performance and capturing the underlying structural information within the data.© 2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.0801 Artificial Intelligence and Image Processing0906 Electrical and Electronic Engineering1005 Communications TechnologiesNetworking & Telecommunications4006 Communications engineering4009 Electronics, sensors and digital hardware4603 Computer vision and multimedia computationJournal ArticleOpenAccess10.1109/lsp.2025.3569476