arXiv:2606. 29261v1 Announce Type: new Abstract: We derive the linear union-of-subspaces (UoS) model for subspace clustering (SC) from the nonlinear mixture model (NMM) used in blind source separation (BSS) to represent a D-dimensional observation vector as an unknown multivariate nonlinear mapping of C latent variables.
By Ivica Kopriva
The paper introduces a multivariate pseudo‑Voigt mixture model, combining Gaussian and Cauchy components with shared location and scale parameters, for robust clustering and outlier detection. Parameter estimation is performed using an EM algorithm that leverages latent variables for efficient likelihood inference. The authors evaluate the model through simulations and real data, comparing it to established robust mixtures such as contaminated normals, and demonstrate its effectiveness on heavy‑tailed datasets.
By Babak F. Dehkordi, Jeffrey L. Andrews, Andrew Jirasek
arXiv:2504. 19419v3 Announce Type: replace Abstract: Local clustering aims to identify specific substructures within a large graph without any additional structural information of the graph.
By Zhaiming Shen, Sung Ha Kang
arXiv:2608.29102v1 Announce Type: new
Abstract: This paper develops a unified theoretical framework showing that a broad family of clustering methods, including k-means, fuzzy c-means, kernel k-means...
By Angshul Majumdar
arXiv:2505.18918v4 Announce Type: replace-cross
Abstract: Principal component analysis (PCA) is a key tool in the field of data dimensionality reduction. Various methods have been proposed to extend...
By Javier Salazar Cavazos, Jeffrey A Fessler, Laura Balzano
arXiv:2608. 14215v1 Announce Type: new Abstract: Constrained optimization extends classical optimization by integrating side information, making it widely applicable across scientific and engineering domains.
By Johanna Hillebrand, Jan H\"ockendorff, J\"urgen Kusche, Kelin Luo, Heiko R\"oglin, Melanie Schmidt, Christian Sohler, Bernd Uebbing