101 citations · 615 across the 44 of their papers we have counts for
5 papers · 1 filter
Deep Unsupervised Clustering Using Mixture of Autoencoders
Dejiao Zhang, Yifan Sun, Brian Eriksson +1
Unsupervised clustering is one of the most fundamental challenges in machine learning. A popular hypothesis is that data are generated from a union of low-dimensional nonlinear man…
Subspace Clustering using Ensembles of -Subspaces
John Lipor, David Hong, Yan Shuo Tan +1
Subspace clustering is the unsupervised grouping of points lying near a union of low-dimensional linear subspaces. Algorithms based directly on geometric properties of such data te…
Algebraic Variety Models for High-Rank Matrix Completion
Greg Ongie, Rebecca Willett, Robert D. Nowak +1
We consider a generalization of low-rank matrix completion to the case where the data belongs to an algebraic variety, i.e. each data point is a solution to a system of polynomial…
Asymptotic performance of PCA for high-dimensional heteroscedastic data
David Hong, Laura Balzano, Jeffrey A. Fessler
Principal Component Analysis (PCA) is a classical method for reducing the dimensionality of data by projecting them onto a subspace that captures most of their variation. Effective…
Real-Time Energy Disaggregation of a Distribution Feeder's Demand Using Online Learning
Gregory S. Ledva, Laura Balzano, Johanna L. Mathieu
Though distribution system operators have been adding more sensors to their networks, they still often lack an accurate real-time picture of the behavior of distributed energy reso…