no code implementations • 18 Apr 2023 • Tengyao Wang, Edgar Dobriban, Milana Gataric, Richard J. Samworth
We propose a new method for high-dimensional semi-supervised learning problems based on the careful aggregation of the results of a low-dimensional procedure applied to many axis-aligned random projections of the data.
no code implementations • 20 Feb 2020 • Milana Gataric
In this paper, we introduce a computational framework for recovering a high-resolution approximation of an unknown function from its low-resolution indirect measurements as well as high-resolution training observations by merging the frameworks of generalized sampling and functional principal component analysis.
no code implementations • 15 Dec 2017 • Milana Gataric, Tengyao Wang, Richard J. Samworth
We introduce a new method for sparse principal component analysis, based on the aggregation of eigenvector information from carefully-selected axis-aligned random projections of the sample covariance matrix.
