  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.
