Two Dimensional Stochastic Configuration Networks for Image Data Analytics

2018/09/06 by Ming Li, Li, Ming, Dianhui Wang +1
Computer Science · #FOS: Computer and information sciences #Face and Expression Recognition #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and ELM #Neural Networks and Applications

paper · pdf · doi:10.48550/arxiv.1809.02066

openalex publication_date 2018/09/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Stochastic configuration networks (SCNs) as a class of randomized learner model have been successfully employed in data analytics due to its universal approximation capability and fast modelling property. The technical essence lies in stochastically configuring hidden nodes (or basis functions) based on a supervisory mechanism rather than data-independent randomization as usually adopted for building randomized neural networks. Given image data modelling tasks, the use of one-dimensional SCNs potentially demolishes the spatial information of images, and may result in undesirable performance. This paper extends the original SCNs to two-dimensional version, termed 2DSCNs, for fast building randomized learners with matrix-inputs. Some theoretical analyses on the goodness of 2DSCNs against SCNs, including the complexity of the random parameter space, and the superiority of generalization, are presented. Empirical results over one regression, four benchmark handwritten digits classification, and two human face recognition datasets demonstrate that the proposed 2DSCNs perform favourably and show good potential for image data analytics.

Citations

• Improving neural networks by preventing co-adaptation of feature detectors
• Stochastic choice of basis functions in adaptive function approximation and the functional-link net
• Gradient-based learning applied to document recognition
• On the Generalization Error Bounds of Neural Networks under Diversity-Inducing Mutual Angular Regularization
• Recovery Guarantees for One-hidden-layer Neural Networks
• Learning Less-Overlapping Representations
• Matrix Neural Networks

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