Aug 2026· Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2· pp. 3919-3930· 0 citations· 72 references
TL;DR
This paper proposes Masked Mixture Factorization (MMF), a lightweight yet effective MF variant that adapts to heterogeneous interactions through instance-wise latent gating, substantially improving accuracy under the same parameter budget while retaining MF's scalability.
Abstract
Matrix factorization (MF) is a widely used backbone for modeling large relational data due to its simplicity, scalability, and interpretability. However, classical MF uses a single shared latent basis, which can be overly restrictive for heterogeneous matrices. In this paper, we propose Masked Mixture Factorization (MMF), a lightweight yet effective MF variant that adapts to heterogeneous interactions through instance-wise latent gating, substantially improving accuracy under the same parameter budget while retaining MF's scalability. We provide theoretical results on MMF's expressivity and identifiability, clarifying when masking expands representational power and when the model is recoverable. Extensive experiments on matrix reconstruction, matrix completion, and Top-N recommendation show consistent gains over strong baselines.
NMF-LAB provides a unified, probabilistic, and scalable framework for classification based on nonnegative matrix factorization, which gives rise to two complementary designs that emphasize feature-level interpretability and competitive predictive accuracy.
Kenichi Satoh· Japanese Journal of Statisti...· 1 citation
An innovative MVC method utilizing orthogonal non-negative anchor tensor factorization, termed ATFMC, which employs a shared-nearest-neighbor density peaks clustering algorithm, which integrates cross-view features to select high-quality anchors and achieves superior clustering performance.
Jia-Yi Wang, Ming Yang, Jing-Yu Wang et al.· ACM Transactions on Intellig...· 0 citations
A novel NMF-based framework that incorporates supervised information to enhance class discriminability in the learned latent space is proposed and explicitly preserves the local geometric structure of the data during factorization, thereby improving the fidelity of the learned representation.
H. Moayed· Acta Universitatis Sapientia...· 0 citations
Simulation studies confirm that the federated approach improves estimation and prediction over purely local methods, especially when per-client data are scarce, and an MRI-based ADHD study illustrates its strong performance under real privacy constraints.
This work develops efficient algorithms based on the difference-of-convex function algorithm (DCA) and the alternating direction method of multipliers (ADMM) to enhance sparsity and identifiability of the learned factors in separable nonnegative matrix factorization.
This paper formalizes an individualized sparse regression framework for matrix-valued covariates in which each observation has its own rows of interest, while the associated regression effects are shared across the population.
Bo-Rui Peng, Li-Wei Lin, Fei-Fei Wang et al.· 0 citations
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