We propose a dimension-reduction method based on the aggregation of localized estimators. The dual process of localization and aggregation helps to mitigate the bias due to the symmetry in the ...

This is a preview. Log in through your library . Abstract Sufficient dimension reduction is popular for reducing data dimensionality without stringent model assumptions. However, most existing methods ...

With the aggressive scaling of semiconductor devices, the increasing complexity of device structure coupled with tighter metrology error budget has driven up Optical ...

Now that you have a solid foundation in Supervised Learning, we shift our attention to uncovering the hidden structure from unlabeled data. We will start with an introduction to Unsupervised Learning.