CGCN-FMF:1D convolutional neural network based feature fusion and multi graph fusion for semi-supervised learning

Multi-view data significantly improves the accuracy of machine learning algorithms by providing a holistic representation of object features. However, previous research on the use of Graph Convolutional Networks (GCNs) for processing node connectivity and data features is still limited. Current methods mainly focus on weighted summation of graph matrices, while only a few integrate the features into graphs. To address these challenges, this paper presents an integrated deep learning architecture: the Feature Fusion and Multi-Graph Fusion Learning Framework (CGCN-FMF). The framework consists of two key modules: (1) a Feature Fusion Network designed to extract essential features from multiple views, and (2) a Multi-Graph Fusion Network that constructs multiple graphs for each view and optimizes both the graph weights and the GCN model. Experimental results from multiple multi-view datasets show that CGCN-FMF outperforms state-of-the-art methods on semi-supervised multi-view classification tasks. • We address deep graph-based semi-supervised classification for multi-view data. • Data structures are set using both KNN graphs and semi-supervised graph schemes. • Autoencoders and 1D convolution layers are leveraged to learn unified features. • A learnable attention mechanism and staged training approach are employed. • Extensive experiments are conducted on six publicly available multi-view datasets.