4 papers
Streaming Heteroscedastic Probabilistic PCA with Missing Data
Kyle Gilman, David Hong, Jeffrey A. Fessler +1
Streaming principal component analysis (PCA) is an integral tool in large-scale machine learning for rapidly estimating low-dimensional subspaces from very high-dimensional data ar…
A Semidefinite Relaxation for Sums of Heterogeneous Quadratic Forms on the Stiefel Manifold
Kyle Gilman, Sam Burer, Laura Balzano
We study the maximization of sums of heterogeneous quadratic forms over the Stiefel manifold, a nonconvex problem that arises in several modern signal processing and machine learni…
HePPCAT: Probabilistic PCA for Data with Heteroscedastic Noise
David Hong, Kyle Gilman, Laura Balzano +1
Principal component analysis (PCA) is a classical and ubiquitous method for reducing data dimensionality, but it is suboptimal for heterogeneous data that are increasingly common i…
Grassmannian Optimization for Online Tensor Completion and Tracking with the t-SVD
Kyle Gilman, Davoud Ataee Tarzanagh, Laura Balzano
We propose a new fast streaming algorithm for the tensor completion problem of imputing missing entries of a low-tubal-rank tensor using the tensor singular value decomposition (t-…