101 citations · 615 across the 44 of their papers we have counts for
8 papers · 1 filter
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…
Matrix Completion over Finite Fields: Bounds and Belief Propagation Algorithms
Mahdi Soleymani, Qiang Liu, Hessam Mahdavifar +1
We consider the low rank matrix completion problem over finite fields. This problem has been extensively studied in the domain of real/complex numbers, however, to the best of auth…
Learning physics-based reduced-order models from data using nonlinear manifolds
Rudy Geelen, Laura Balzano, Stephen Wright +1
We present a novel method for learning reduced-order models of dynamical systems using nonlinear manifolds. First, we learn the manifold by identifying nonlinear structure in the d…
ALPCAH: Sample-wise Heteroscedastic PCA with Tail Singular Value Regularization
Javier Salazar Cavazos, Jeffrey A. Fessler, Laura Balzano
Principal component analysis (PCA) is a key tool in the field of data dimensionality reduction that is useful for various data science problems. However, many applications involve…
Learning latent representations in high-dimensional state spaces using polynomial manifold constructions
Rudy Geelen, Laura Balzano, Karen Willcox
We present a novel framework for learning cost-efficient latent representations in problems with high-dimensional state spaces through nonlinear dimension reduction. By enriching l…
Dynamic Subspace Estimation with Grassmannian Geodesics
Cameron J. Blocker, Haroon Raja, Jeffrey A. Fessler +1
Dynamic subspace estimation, or subspace tracking, is a fundamental problem in statistical signal processing and machine learning. This paper considers a geodesic model for time-va…
HeMPPCAT: Mixtures of Probabilistic Principal Component Analysers for Data with Heteroscedastic Noise
Alec S. Xu, Laura Balzano, Jeffrey A. Fessler
Mixtures of probabilistic principal component analysis (MPPCA) is a well-known mixture model extension of principal component analysis (PCA). Similar to PCA, MPPCA assumes the data…
A Proximal DC Algorithm for Sample Average Approximation of Chance Constrained Programming
Peng Wang, Rujun Jiang, Qingyuan Kong +1
Chance constrained programming (CCP) refers to a type of optimization problem with uncertain constraints that are satisfied with at least a prescribed probability level. In this wo…