arXiv:2512. 02653v2 Announce Type: replace Abstract: Multi-view learning integrates diverse representations of the same instances and can improve performance when interactions across views are effectively exploited.
arXiv:2607. 18561v1 Announce Type: new Abstract: In recent years, multi-view learning has attracted increasing attention, as it integrates the complementary information of heterogeneous views.
arXiv:2504. 18455v2 Announce Type: replace-cross Abstract: We study distributed multiview representation learning, a problem in which $K$ clients each observe a distinct but possibly statistically correlated view.
The paper introduces SIMPLE, a prior‑fitted multi‑view in‑context learner that learns a reusable, task‑conditioned inference procedure instead of a fixed fusion function. By generating synthetic task priors in embedding space, SIMPLE can handle diverse view configurations, class structures, and missingness patterns. Experiments on multi‑view and multi‑omics benchmarks show that a frozen SIMPLE model performs competitively, and lightweight adapter calibration further improves performance across most datasets.
arXiv:2607. 23149v1 Announce Type: new Abstract: Random Vector Functional Link (RVFL) networks provide an efficient randomized learning framework for classification.
arXiv:2606. 05814v1 Announce Type: new Abstract: The support vector machine (SVM) is a widely used classifier, but choosing an appropriate loss function remains difficult.
arXiv:2609.25811v1 Announce Type: new Abstract: Multi-view clustering (MVC) aims to uncover latent cluster structures by exploiting complementary information from multiple views. Despite substantial...
The paper presents a semi‑supervised generative model for multi‑view learning that handles missing views and missing labels. It combines a likelihood‑based approach for unlabeled data with an information bottleneck (IB) framework for labeled data, incorporating modality‑specific information and cross‑view mutual information maximization to learn a shared latent space. Experiments show improved predictive and generative performance on complex datasets with limited labeled samples.
The paper introduces Trusted Multi-view learning with Unified Routing (TMUR), a method that separates view-specific evidence extraction from fusion arbitration in multi-view classification. TMUR employs view-private experts, a collaborative expert, and a unified router that assigns sample-level weights based on global context, along with soft load-balancing and diversity regularization to promote balanced and discriminative expert use. Experiments on 14 datasets show that TMUR consistently improves classification accuracy and reliability compared to 15 recent baselines.
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.
arXiv:2609.39019v1 Announce Type: new Abstract: Multi-view learning seeks to learn more comprehensive representations by exploiting the complementarity and consistency across diverse modalities or vi...
The paper introduces a divergence-based similarity function (DSF) for multi-view contrastive learning, representing each set of augmented views as a distribution and measuring similarity via distribution divergence. DSF captures joint structure across all views, outperforming prior pairwise methods on tasks such as kNN classification, linear evaluation, transfer learning, and distribution shift. It also offers greater efficiency and eliminates the need for a temperature hyperparameter, unlike cosine similarity.
The paper introduces CELM, a data‑free framework for federated learning that estimates class‑wise contribution by maximizing logits. It constructs a cross‑client evidence matrix to quantify each client’s competence and coverage for each class, then uses this matrix to compute weighted aggregation that upweights clients offering strong evidence for underrepresented classes. The method maintains stability through simplex constraints and momentum smoothing, and it is compatible with standard FL pipelines, showing improved robustness to class imbalance and heterogeneity on vision benchmarks.