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arXiv:2412. 07041v4 Announce Type: replace-cross Abstract: Recovering incomplete multidimensional tensor-structured data is a fundamental task in many real-world applications.
arXiv:2607. 03641v1 Announce Type: cross Abstract: The manifold hypothesis posits that high-dimensional data are concentrated near a low-dimensional embedded manifold.
arXiv:2606. 31253v1 Announce Type: cross Abstract: The classical $k$-means clustering, based on distances computed from all data features, cannot be directly applied to incomplete data with missing values.
The paper introduces the Robust Graph Clustering Network for Multiple Missing Data (RGCN), a method designed to cluster graphs with simultaneous missing node attributes and structural links. RGCN employs a view‑decoupled dual‑branch imputation to reduce cross‑view interference, a multi‑hyperspherical mixture prior to improve cluster compactness and separability on a directional latent manifold, and a boundary‑aware contrastive enhancement objective to counteract cluster blurring caused by imputation bias. Experiments on real‑world datasets show that RGCN consistently outperforms state‑of‑the‑art baselines across various missing data patterns.
arXiv:2606. 06328v1 Announce Type: new Abstract: In healthcare, multimodal time series tasks often operate on incomplete observations in practice, for example when ECG segments are lost because electrodes detach or an entire respiratory channel is unavailable during overnight monitoring.
arXiv:2609.39613v1 Announce Type: new Abstract: Missing data are a fundamental challenge in statistical analysis and machine learning, as the choice of imputation method substantially impacts downstr...