A Novel Non-iterative Training Method for CNN Classifiers Using Gram–Schmidt Process

Abstract Convolutional neural networks have become prominent machine learning models, particularly in the realm of computer vision, due to their ability to predict and extract robust features from raw image data. CNNs, similar to other neural network models, undergo training via backpropagation, an iterative technique. However, the backpropagation algorithm has notable challenges, including slow convergence, susceptibility to local minima, and hypersensitivity to learning rates. These challenges not only impact the model’s accuracy but also make the training process computationally intensive. To address these limitations, We introduce a novel approach that trains the CNN classifier using a non-iterative learning method. The proposed approach involves automatic extraction of pertinent features from the raw-data, followed by the application of Gram–Schmidt process to decompose the feature matrix and determine classifier’s weights. The proposed method has shown enhanced predictive accuracy over state-of-the-art models when evaluated on two benchmark datasets, MNIST and CIFAR-10. The extensive experimentation using most cited pre-trained experiments validate the effectiveness of our proposed method.