MBGDT:Robust Mini-Batch Gradient Descent
This addresses robustness issues for machine learning practitioners dealing with outlier-prone data, though it appears incremental as it builds on existing gradient descent techniques.
The paper tackles the problem of machine learning models being fragile to outliers in high dimensions by introducing MBGDT, a method based on mini-batch gradient descent with trimming, which shows state-of-the-art performance and greater robustness than baselines on a designed dataset.
In high dimensions, most machine learning method perform fragile even there are a little outliers. To address this, we hope to introduce a new method with the base learner, such as Bayesian regression or stochastic gradient descent to solve the problem of the vulnerability in the model. Because the mini-batch gradient descent allows for a more robust convergence than the batch gradient descent, we work a method with the mini-batch gradient descent, called Mini-Batch Gradient Descent with Trimming (MBGDT). Our method show state-of-art performance and have greater robustness than several baselines when we apply our method in designed dataset.