Multi-Fractional Gradient Descent: A Novel Approach to Gradient Descent for Robust Linear Regression
Year: 2024
Type: article
Abstract: Authors: This work introduces a novel gradient descent method by generalizing the fractional gradient descent (FGD) such that instead of the same fractional order for all variables, we assign different fractional orders to each variable depending on its characteristics and its relation to other variables. We name this method Multi-Fractional Gradient Descent (MFGD) and by using it in linear regression for minimizing loss function (residual sum of square) and apply it on four financial time series data and also tuning their hyperparameters, we can observe that unlike GD and FGD, MFGD is robust to multicollinearity in the data and also can detect the real information in it and obtain considerable lower error.
Source: Engineering World
Institution: Khatam University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4403401134