Abstract Self-expressive subspace clustering methods perform well in processing high-dimensional data and are one of the key techniques in this field. However, traditional self-expressive models typically assume that the dataset is static, and it is difficult to adapt to dynamic, continuously arriving data streams. It leads to two issues: feature heterogeneity between novel data and old data and the inclusion of unknown novel classes in newly arriving samples. To address these problems, a subspace clustering framework, joint self-expressive subspace clustering method(JSSC), is proposed. JSSC is specifically designed to handle both old and novel category samples. JSSC adapts to the continuous arrival of data streams by combining a self-expressive feature learning module with a novel category sample processing module. The proposed method effectively clusters novel category samples while maintaining strong performance on existing categories. Additionally, a deep autoencoder is utilized to learn subspace basis. Thus intuitive and interpretable representations are achieved, and the known and emerging categories are simultaneously managed through pairwise objectives and regularization terms. Experimental results on benchmark datasets show that JSSC outperforms current state-of-the-art approaches in clustering tasks, particularly excelling in handling novel categories within dynamic data.
Fund:National Natural Science Foundation of China(No.62006131,62203238,62071260), Natural Science Foundation of Zhejiang Province(No.LZ22F020001,LQ21F020009,LQ18F020001), 2025 Key Technological Innovation Program of Ningbo City(No.2023Z224)
Corresponding Authors:
ZHAO Jieyu, Ph.D., professor. His research interests include image graphics technology, natural interaction, machine learning, and computer vision.
About author:: ZHANG Hantao, Master student. His research interests include computer vision, unsupervised learning, and cluster analysis. YE Xulun, Ph.D., professor. His research interests include Bayesian learning, nonparametric clustering, and convex analysis.
[1] VIDAL R. Subspace Clustering. IEEE Signal Processing Magazine, 2011, 28(2): 52-68. [2] ZHANG Z, LIU B, SHAO J M.Fine-Tuning Happens in Tiny Subspaces: Exploring Intrinsic Task-Specific Subspaces of Pre-trained Language Models//Proc of the 61st Annual Meeting of the Association for Computational Linguistics(Long Papers). Stroudsburg, USA: ACL, 2023: 1701-1713. [3] ZHOU Z L, DING C, LI J, et al. Sequential Order-Aware Coding-Based Robust Subspace Clustering for Human Action Recognition in Untrimmed Videos. IEEE Transactions on Image Processing, 2023, 32: 13-28. [4] ZHANG C Q, FU H Z, HU Q H, et al. Generalized Latent Multi-view Subspace Clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(1): 86-99. [5] CUI Z H, JING X C, ZHAO P, et al. A New Subspace Clustering Strategy for AI-Based Data Analysis in IoT System. IEEE Internet of Things Journal, 2021, 8(16): 12540-12549. [6] JI P, ZHANG T, LI H D, et al. Deep Subspace Clustering Networks//Proc of the 31st International Conference on Neural Infor-mation Processing Systems. Cambridge, USA: MIT Press, 2017: 23-32. [7] ZHOU P, HOU Y Q, FENG J S.Deep Adversarial Subspace Clustering//Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2018: 1596-1604. [8] LÜ J C, KANG Z, LU X, et al. Pseudo-Supervised Deep Subspace Clustering. IEEE Transactions on Image Processing, 2021, 30: 5252-5263. [9] ZHANG J J, LI C G, YOU C, et al. Self-Supervised Convolutional Subspace Clustering Network//Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2019: 5468-5477. [10] HUANG P H, HUANG Y, WANG W, et al. Deep Embedding Net-work for Clustering//Proc of the 22nd International Conference on Pattern Recognition. Washington, USA: IEEE, 2014: 1532-1537. [11] LI J, LIU H F, ZHAO H D, et al. Projective Low-Rank Subspace Clustering via Learning Deep Encoder//Proc of the 26th International Joint Conference on Artificial Intelligence Main Track. San Francisco, USA: IJCAI, 2017: 2145-2151. [12] CHANG J L, MENG G F, WANG L F, et al. Deep Self-Evolution Clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(4): 809-823. [13] WU J L, LONG K Y, WANG F, et al. Deep Comprehensive Correlation Mining for Image Clustering//Proc of the IEEE/CVF International Conference on Computer Vision. Washington, USA: IEEE, 2019: 8149-8158. [14] HUANG J B, GONG S G, ZHU X T.Deep Semantic Clustering by Partition Confidence Maximisation//Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington,USA: IEEE, 2020: 8846-8855. [15] RONEN M, FINDER S E, FREIFELD O.DeepDPM: Deep Clustering with an Unknown Number of Clusters//Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington,USA: IEEE, 2022: 9851-9860. [16] ELHAMIFAR E, VIDAL R.Sparse Subspace Clustering: Algorithm, Theory, and Applications. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(11): 2765-2781. [17] FAN J C, YANG C R, UDELL M.Robust Non-linear Matrix Factorization for Dictionary Learning, Denoising, and Clustering. IEEE Transactions on Signal Processing, 2021, 69: 1755-1770. [18] FAN J C.PATE L V M, VIDAL R. Kernel Sparse Subspace Clustering//Proc of the IEEE International Conference on Image Processing. Washington, USA: IEEE, 2014: 2849-2853. [19] FAVARO P, VIDAL R, RAVICHANDRAN A.A Closed form Solution to Robust Subspace Estimation and Clustering//Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington,USA: IEEE, 2011: 1801-1807. [20] LU C Y, MIN H, ZHAO Z Q, et al. Robust and Efficient Subspace Segmentation via Least Squares Regression//Proc of the 12th European Conference on Computer Vision. Berlin, Germany: Springer, 2012: 347-360. [21] PENG X, FENG J S, XIAO S J, et al. Structured Autoencoders for Subspace Clustering. IEEE Transactions on Image Processing, 2018, 27(10): 5076-5086. [22] ZHANG S Z, YOU C, VIDAL R, et al. Learning a Self-Expressive Network for Subspace Clustering//Proc of the IEEE/CVF Confe-rence on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2021: 12388-12398. [23] HSU Y C, LÜ Z Y, KIRA Z.Learning to Cluster in Order to Transfer Across Domains and Tasks[C/OL].[2024-10-27]. https://arxiv.org/pdf/1711.10125. [24] HSU Y C, LÜ Z Y, SCHLOSSER J, et al. Multi-class Classification without Multi-class Labels[C/OL].[2024-10-27]. https://arxiv.org/pdf/1901.00544. [25] ZHONG Z, FINI E, ROY S, et al. Neighborhood Contrastive Lear-ning for Novel Class Discovery//Proc of the IEEE/CVF Confe-rence on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2021: 10862-10870. [26] HAN K, REBUFFI S A, EHRHARDT S, et al. AutoNovel: Automatically Discovering and Learning Novel Visual Categories. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(10): 6767-6781. [27] ZHANG S, KHAN S, SHEN Z Q, et al. PromptCAL: Contrastive Affinity Learning via Auxiliary Prompts for Generalized Novel Category Discovery//Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2023: 3479-3488. [28] GU P Y, ZHANG C Y, XU R J, et al. Class-Relation Knowledge Distillation for Novel Class Discovery//Proc of the IEEE/CVF International Conference on Computer Vision. Washington, USA: IEEE, 2023: 16428-16437. [29] TAO X Y, HONG X P, CHANG X Y, et al. Few-Shot Class-Incremental Learning//Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2020: 12180-12189. [30] NAEEM M F, XIAN Y Q, TOMBARI F, et al. Learning Graph Embeddings for Compositional Zero-Shot Learning//Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2021: 953-962. [31] LI W Y, LIU J, HAN B, et al. Adjustment and Alignment for Unbiased Open Set Domain Adaptation//Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2023: 24110-24119. [32] YOU C, ROBINSON D P, VIDAL R.Scalable Sparse Subspace Clustering by Orthogonal Matching Pursuit//Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2016: 3918-3927. [33] JI P, SALZMANN M, LI H D.Efficient Dense Subspace Clustering//Proc of the IEEE Winter Conference on Applications of Computer Vision. Washington, USA: IEEE, 2014: 461-468. [34] YANG X, DENG C, ZHENG F, et al. Deep Spectral Clustering Using Dual Autoencoder Network//Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2019: 4061-4070. [35] PENG Z H, LIU H, JIA Y H, et al. Adaptive Attribute and Structure Subspace Clustering Network. IEEE Transactions on Image Processing, 2022, 31: 3430-3439. [36] HAN K, VEDALDI A, ZISSERMAN A.Learning to Discover Novel Visual Categories via Deep Transfer Clustering//Proc of the IEEE/CVF International Conference on Computer Vision. Wa-shington, USA: IEEE, 2019: 8400-8408. [37] RIZVE M N, KARDAN N, SHAH M.Towards Realistic Semi-Supervised Learning//Proc of the European Conference on Computer Vision. Berlin, Germany: Springer, 2022: 437-455.
2025, Vol. 38 
