Deep learning model construction for a semi-supervised classification with feature learning

Several deep models were proposed in image processing, data interpretation, speech recognition, and video analysis. Most of these architectures need a massive proportion of training samples and use arbitrary configuration. This paper constructs a deep learning architecture with feature learning. Gra...

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Bibliographic Details
Published in:Complex & intelligent systems 2023-06, Vol.9 (3), p.3011-3021
Main Authors: Mandapati, Sridhar, Kadry, Seifedine, Kumar, R. Lakshmana, Sutham, Krongkarn, Thinnukool, Orawit
Format: Article
Language:English
Subjects:
Algorithms
Classification
Complexity
Computational Intelligence
Convolution
Data Structures and Information Theory
Deep architecture
Deep learning
Engineering
Feature learning
Graphical representations
Image processing
Machine learning
Nodes
Original Article
Parameter modification
Principal components analysis
Semi-supervised classification
Semi-supervised learning
Speech recognition
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Summary:Several deep models were proposed in image processing, data interpretation, speech recognition, and video analysis. Most of these architectures need a massive proportion of training samples and use arbitrary configuration. This paper constructs a deep learning architecture with feature learning. Graph convolution networks (GCNs), semi-supervised learning and graph data representation, have become increasingly popular as cost-effective and efficient methods. Most existing merging node descriptions for node distribution on the graph use stabilised neighbourhood knowledge, typically requiring a significant amount of variables and a high degree of computational complexity. To address these concerns, this research presents DLM-SSC, a unique method semi-supervised node classification tasks that can combine knowledge from multiple neighbourhoods at the same time by integrating high-order convolution and feature learning. This paper employs two function learning techniques for reducing the number of parameters and hidden layers: modified marginal fisher analysis (MMFA) and kernel principal component analysis (KPCA). The MMFA and KPCA weight matrices are modified layer by layer when implementing the DLM, a supervised pretraining technique that doesn't require a lot of information. Free measuring on citation datasets (Citeseer, Pubmed, and Cora) and other data sets demonstrate that the suggested approaches outperform similar algorithms.
ISSN:2199-4536
2198-6053
DOI:10.1007/s40747-022-00641-9