arXiv:2505. 20532v2 Announce Type: replace Abstract: This paper studies robust one-shot aggregation for distributed and federated Independent Component Analysis (ICA).
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
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.
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...
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.
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.
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.
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.