Data Transformation-Driven Fuzzy Clustering Neural Network With Layerwise and End-to-End Training

In this study, we propose a novel data transformation-driven fuzzy clustering neural network (DTFCNN) to enhance the dimensionality reduction function and end-to-end refinment learning ability of the entire structure. Unlike conventional fuzzy clustering-based neural networks, which rely on fuzzy c-means clustering in the hidden layer and least squares error-based learning for connection weights, the DTFCNN utilizes backpropagation (BP) learning as a refinement algorithm. This refinement process enables iterative fine-tuning of the model’s parameters, allowing it to adapt more effectively to complex patterns. By using BP, DTFCNN enhances its ability to learn intricate feature interactions and extract relevant features from high-dimensional spaces, thereby significantly improving the model’s flexibility and performance. The proposed DTFCNN consists of four layers. First, a preprocessing layer employs principal component analysis for feature extraction and dimensionality reduction, where eigenvectors are used as connection weights for the preprocessing layer. Second, a hidden layer utilizes fuzzy c-means clustering for generating fuzzy membership degrees, and centers serve as connection weights for hidden layers. The entries of the partition matrix also are regarded as membership degrees. In the output layer, a linear-driven affine transform is used for fitting connection weights, and a SoftMax function is employed to express the outputs as probabilities. Finally, all parameters, such as eigenvectors, centers, and coefficients, from the preprocessing layer to the output layer are refined through BP-based learning. To validate the effectiveness of the DTFCNN, we conducted a collection of comparison experiments: 1) publicly benchmark datasets with statistical analysis, 2) facial recognition datasets for application-specific testing, and 3) three large-scale datasets. The results demonstrate that the DTFCNN outperforms classical classifiers, state-of-the-art fuzzy classifiers, and deep learning baselines in terms of accuracy and generalization capability. The DTFCNN model uses fewer parameters than deep learning baselines, resulting in faster training times without compromising performance. Overall, the DTFCNN achieves higher accuracy while maintaining model adaptability.

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