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Multi-view semi-supervised classification via innovative graph construction and smoothness-aware graph convolution.

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Title: Multi-view semi-supervised classification via innovative graph construction and smoothness-aware graph convolution.
Authors: Peng, G.1 (AUTHOR) gpeng001@ikasle.ehu.eus, Dornaika, F.1,2 (AUTHOR) fadi.dornaika@ehu.eus, Hamad, D.3 (AUTHOR) denis.hamad@gmail.com, Hoang, V.T.3 (AUTHOR) vinh.th@ou.edu.vn
Source: Knowledge-Based Systems. May2026, Vol. 340, pN.PAG-N.PAG. 1p.
Subjects: Graph neural networks, Autoencoders, Supervised learning, K-nearest neighbor classification, Loss functions (Statistics)
Abstract: Multi-view semi-supervised classification (Mv-SSC) aims to leverage complementary information from multiple views and unlabeled data to enhance classification performance. However, existing methods often struggle with constructing robust graphs, effectively fusing heterogeneous features. Therefore, we propose a novel framework, Multi-view Semi-supervised Classification with Graph Construction Innovation and Smoothness-aware Graph Convolution (GCSGC), which introduces three key innovations. First, we employ sparse autoencoders (SAEs) coupled with a learnable feature fusion module based on a one-layer fully connected network. The SAEs extract compact and discriminative representations from each view by enforcing sparsity to remove redundancies, while the fully connected layer adaptively aligns the multi-view latent features with the learnable shared features to preserve complementary semantic information. Second, GCSGC introduces two types of view-based graphs: (1) a Cosine-KNN-based Collaborative Graph (CKG) that integrates K-Nearest Neighbor relationships with cosine similarity of the learned view-specific latent features, and (2) a robust semi-supervised graph that enhances inter-view consistency and structural stability. Third, we design a hybrid loss function that combines cross-entropy and smoothness regularization to guide the optimization of the graph convolutional network. Extensive experiments on multiple benchmark datasets demonstrate that GCSGC consistently outperforms state-of-the-art Mv-SSC methods, validating the effectiveness of its innovative graph construction strategy, sparse feature fusion mechanism, and dual-loss optimization. This work provides a new perspective on integrating structural modeling, feature transformation, and loss design in multi-view semi-supervised learning. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:Multi-view semi-supervised classification (Mv-SSC) aims to leverage complementary information from multiple views and unlabeled data to enhance classification performance. However, existing methods often struggle with constructing robust graphs, effectively fusing heterogeneous features. Therefore, we propose a novel framework, Multi-view Semi-supervised Classification with Graph Construction Innovation and Smoothness-aware Graph Convolution (GCSGC), which introduces three key innovations. First, we employ sparse autoencoders (SAEs) coupled with a learnable feature fusion module based on a one-layer fully connected network. The SAEs extract compact and discriminative representations from each view by enforcing sparsity to remove redundancies, while the fully connected layer adaptively aligns the multi-view latent features with the learnable shared features to preserve complementary semantic information. Second, GCSGC introduces two types of view-based graphs: (1) a Cosine-KNN-based Collaborative Graph (CKG) that integrates K-Nearest Neighbor relationships with cosine similarity of the learned view-specific latent features, and (2) a robust semi-supervised graph that enhances inter-view consistency and structural stability. Third, we design a hybrid loss function that combines cross-entropy and smoothness regularization to guide the optimization of the graph convolutional network. Extensive experiments on multiple benchmark datasets demonstrate that GCSGC consistently outperforms state-of-the-art Mv-SSC methods, validating the effectiveness of its innovative graph construction strategy, sparse feature fusion mechanism, and dual-loss optimization. This work provides a new perspective on integrating structural modeling, feature transformation, and loss design in multi-view semi-supervised learning. [ABSTRACT FROM AUTHOR]
ISSN:09507051
DOI:10.1016/j.knosys.2026.115586