Phase-Consistent Magnetic Spectral Learning for Multi-View Clustering proposes a new unsupervised method that uses a complex-valued magnetic affinity to capture both magnitude and phase information across multiple data views. By deriving a confidence-directed cross-view flow from anchor assignments and combining it with a nonnegative magnitude backbone, the approach constructs a Hermitian magnetic Laplacian that yields a stable shared spectral signal for representation learning and clustering. Experiments on ten public benchmarks show the method outperforms or matches leading MVC baselines on most dataset-metric combinations.
By Mingdong Lu, Zhikui Chen, Meng Liu, Shubin Ma, Zhengyang Tang
arXiv:2609.36762v1 Announce Type: new
Abstract: Federated clustering methods that do not require the global number of clusters $K$ still assume that each client knows its local number $K_g$. This ass...
By Mitushi Goyal, Tarun S., Riddhanya Senapathi, Arun Raman
arXiv:2606. 07914v1 Announce Type: cross Abstract: We study component recovery and mixing-matrix estimation from unlabeled finite mixtures whose observable distributions share the same latent components but have unknown mixing weights.
By Takafumi Kanamori, Yushi Hirose, Shohei Yamamoto
arXiv:2608. 08704v1 Announce Type: cross Abstract: Kernel spectral clustering with a single bandwidth can be inadequate for data exhibiting multiple characteristic pairwise-distance scales, a problem particularly prevalent in the high-dimensional regime.
By Zeqin Lin, Guangming Pan, Zhixiang Zhang, Yinbing Zhou
arXiv:2608. 13229v1 Announce Type: cross Abstract: We present the mathematical foundations of linear independent component analysis (ICA) models based on standard literature in a self-contained note.
By Patrick Forr\'e
arXiv:2602. 22568v2 Announce Type: replace-cross Abstract: Deep multi-view clustering has achieved remarkable progress but remains vulnerable to complex noise in real-world applications.
By Peihan Wu, Guanjie Cheng, Yufei Tong, Meng Xi, Shuiguang Deng
arXiv:2601. 09304v2 Announce Type: replace Abstract: Federated Learning (FL) enables distributed learning across multiple clients without sharing raw data.
By Sota Sugawara, Yuji Kawamata, Akihiro Toyoda, Tomoru Nakayama, Yukihiko Okada
arXiv:2602. 23785v2 Announce Type: replace Abstract: We investigate the identifiability of nonlinear canonical correlation analysis (CCA) in a multi-view setup, in which each view is generated by applying an unknown nonlinear map to a linear mixture of shared latent variables plus view-private noise.
By Zhiwei Han, Stefan Matthes, Hao Shen
The paper introduces robust multi-task procedures for principal component analysis that leverage similarity across tasks to enhance eigenspace estimation while remaining resilient to outlier tasks. It establishes non-asymptotic convergence rates and demonstrates that the methods achieve minimax optimal performance across various regimes. One procedure, based on matrix-depth, attains optimal error dependence on the proportion of outlier tasks, addressing a key challenge in robust multi-task learning.
By Dali Liu, Haolei Weng
arXiv:2608. 00621v1 Announce Type: new Abstract: Multimodal-attributed graphs (MAGs), whose nodes carry heterogeneous attributes such as text and images over a relational structure, have become a fundamental substrate for label-free entity grouping tasks, including community discovery and product segmentation.
By Yinlin Zhu, Di Wu, Ziyu Han, Zekai Chenm, Wang Luo, Miao Hu, Guocong Quan
arXiv:2607. 04189v1 Announce Type: new Abstract: Federated Learning (FL) is fundamentally challenged by statistical heterogeneity, where non-identically distributed (non-IID) data induces client drift that severely hampers global convergence.
By Liyang Yuan, Yibo Yang, Dandan Guo, Peter Richtarik, Zhouchen Lin
arXiv:2606. 27984v1 Announce Type: new Abstract: Multimodal feature fusion can effectively capture complex patterns in real-world data by integrating complementary information from different modalities.
By Liang Zhao, Shubin Ma, Bo Xu, Qingchen Zhang