Intuitionistic Fuzzy Graph Embedded Random Vector Functional Link with Multiview Learning

• URL: http://arxiv.org/abs/2607.05635v1
• Date: Mon, 06 Jul 2026 20:54:02 GMT
• Title: Intuitionistic Fuzzy Graph Embedded Random Vector Functional Link with Multiview Learning
• Abstract summary: We propose the Intuitionistic Fuzzy Graph Embedded Random Vector Functional Link with Multiview Learning (IFGRVFL-MV) model.<n>The proposed approach comprises three key components: intuitionistic fuzzy sets for uncertainty handling, graph embedding to capture intrinsic geometric structures, and multiview learning to use complementary information from multiple feature spaces.<n>Our results establish that IFGRVFL-MV is a promising advancement in the domain of uncertainty and multiview environments.
• Score: 5.725795684434675
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Random Vector Functional Link (RVFL) networks are popular due to their fast training and universal approximation capabilities. However, RVFL models face challenges in preserving geometric relationships and utilizing multiple feature views effectively. To address these limitations we propose the Intuitionistic Fuzzy Graph Embedded Random Vector Functional Link with Multiview Learning (IFGRVFL-MV) model. The proposed approach comprises three key components: intuitionistic fuzzy sets for uncertainty handling, graph embedding to capture intrinsic geometric structures, and multiview learning to use complementary information from multiple feature spaces. The model assigns intuitionistic fuzzy membership and non-membership values to data points making it robust to outliers. Also, the graph embedding framework preserves topological structures, increasing the generalization performance. We performed experiments on benchmark datasets from UCI and KEEL repositories which concludes that IFGRVFL-MV outperforms existing models in classification accuracy. Our results establish that IFGRVFL-MV is a promising advancement in the domain of uncertainty and multiview environments.

Related papers

• XGRVFL-MV: Residual-Coupled Graph-Embedded Multi-View Random Vector Functional Link Network with FleXi Guardian Loss [2.2418940049493368]
  This paper proposes a Residual-Coupled Graph-Embedded Multi-View RVFL model with fleXi guardian loss (XGRVFL-MV) for multi-view classification.<n>The proposed model constructs RVFL representation for each view, incorporates graph embedding with intrinsic and penalty graphs constructed using the Local Fisher Discriminant Analysis weighting scheme.<n>A residual-coupling term is introduced to encourage consistency among view-specific prediction residuals while preserving view-specific representations.
  arXiv  Detail & Related papers  (2026-07-25T11:00:23Z)
• Graph-Embedded Intuitionistic Fuzzy Broad Learning System: A Multi-view Framework [2.7594168650194533]
  We propose a Multi-View Graph-Embedded Intuitionistic Fuzzy Broad Learning System (MVGIFBLS)<n>It integrates multi-view learning, graph embedding, and intuitionistic fuzzy theory into the BLS framework.<n>MVGIFBLS consistently achieves higher Area Under the Curve (AUC) scores and maintains robust performance under Gaussian feature noise.
  arXiv  Detail & Related papers  (2026-07-18T09:37:46Z)
• Beyond Softmax: A Natural Parameterization for Categorical Random Variables [61.709831225296305]
  We introduce the $textitcatnat$ function, a function composed of a sequence of hierarchical binary splits.<n>A rich set of experiments show that the proposed function improves the learning efficiency and yields models characterized by consistently higher test performance.
  arXiv  Detail & Related papers  (2025-09-29T12:55:50Z)
• Self-Reinforced Graph Contrastive Learning [7.49025068464945]
  We propose SRGCL (Self-Reinforced Graph Contrastive Learning), a novel framework to dynamically evaluate and select high-quality positive pairs.<n>In experiments on diverse graph-level classification tasks, SRGCL consistently outperforms state-of-the-art GCL methods.
  arXiv  Detail & Related papers  (2025-05-19T18:45:54Z)
• Multi-view Fuzzy Graph Attention Networks for Enhanced Graph Learning [0.0]
  Fuzzy Graph Attention Network (FGAT) has shown promise in tasks requiring robust graph-based learning.<n>We propose the Multi-view Fuzzy Graph Attention Network (MFGAT), a novel framework that constructs and aggregates multi-view information.
  arXiv  Detail & Related papers  (2024-12-23T04:39:08Z)
• GRVFL-MV: Graph Random Vector Functional Link Based on Multi-View Learning [0.2999888908665658]
  A novel graph random vector functional link based on multi-view learning (GRVFL-MV) model is proposed.<n>The proposed model is trained on multiple views, incorporating the concept of multiview learning (MVL)<n>It also incorporates the geometrical properties of all the views using the graph embedding (GE) framework.
  arXiv  Detail & Related papers  (2024-09-07T07:18:08Z)
• Neural Clustering based Visual Representation Learning [61.72646814537163]
  Clustering is one of the most classic approaches in machine learning and data analysis. We propose feature extraction with clustering (FEC), which views feature extraction as a process of selecting representatives from data. FEC alternates between grouping pixels into individual clusters to abstract representatives and updating the deep features of pixels with current representatives.
  arXiv  Detail & Related papers  (2024-03-26T06:04:50Z)
• Joint Feature and Differentiable $ k $-NN Graph Learning using Dirichlet Energy [103.74640329539389]
  We propose a deep FS method that simultaneously conducts feature selection and differentiable $ k $-NN graph learning. We employ Optimal Transport theory to address the non-differentiability issue of learning $ k $-NN graphs in neural networks. We validate the effectiveness of our model with extensive experiments on both synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2023-05-21T08:15:55Z)
• Adversarial Feature Augmentation and Normalization for Visual Recognition [109.6834687220478]
  Recent advances in computer vision take advantage of adversarial data augmentation to ameliorate the generalization ability of classification models. Here, we present an effective and efficient alternative that advocates adversarial augmentation on intermediate feature embeddings. We validate the proposed approach across diverse visual recognition tasks with representative backbone networks.
  arXiv  Detail & Related papers  (2021-03-22T20:36:34Z)
• Probabilistic Graph Attention Network with Conditional Kernels for Pixel-Wise Prediction [158.88345945211185]
  We present a novel approach that advances the state of the art on pixel-level prediction in a fundamental aspect, i.e. structured multi-scale features learning and fusion. We propose a probabilistic graph attention network structure based on a novel Attention-Gated Conditional Random Fields (AG-CRFs) model for learning and fusing multi-scale representations in a principled manner.
  arXiv  Detail & Related papers  (2021-01-08T04:14:29Z)
• Deep Reinforcement Learning of Graph Matching [63.469961545293756]
  Graph matching (GM) under node and pairwise constraints has been a building block in areas from optimization to computer vision. We present a reinforcement learning solver for GM i.e. RGM that seeks the node correspondence between pairwise graphs. Our method differs from the previous deep graph matching model in the sense that they are focused on the front-end feature extraction and affinity function learning.
  arXiv  Detail & Related papers  (2020-12-16T13:48:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.