A New Backpropagation Algorithm without Gradient Descent
This addresses a foundational problem in machine learning by proposing a new training paradigm, potentially impacting all neural network applications.
The paper tackles the problem of neural network training by developing an alternative to backpropagation that eliminates gradient descent, using the Moore-Penrose Pseudo Inverse instead, with results verified through numerical studies and experiments on various datasets.
The backpropagation algorithm, which had been originally introduced in the 1970s, is the workhorse of learning in neural networks. This backpropagation algorithm makes use of the famous machine learning algorithm known as Gradient Descent, which is a first-order iterative optimization algorithm for finding the minimum of a function. To find a local minimum of a function using gradient descent, one takes steps proportional to the negative of the gradient (or of the approximate gradient) of the function at the current point. In this paper, we develop an alternative to the backpropagation without the use of the Gradient Descent Algorithm, but instead we are going to devise a new algorithm to find the error in the weights and biases of an artificial neuron using Moore-Penrose Pseudo Inverse. The numerical studies and the experiments performed on various datasets are used to verify the working of this alternative algorithm.