148 citations · 217 across the 7 of their papers we have counts for
9 papers
Generalized Canonical Polyadic Tensor Decompositions with General Symmetry
Alex Mulrooney, David Hong
Canonical Polyadic (CP) tensor decomposition is a workhorse algorithm for discovering underlying low-dimensional structure in tensor data. This is accomplished in conventional CP d…
Network Signflip Parallel Analysis for Selecting the Embedding Dimension
David Hong, Joshua Cape
This paper investigates the problem of selecting the embedding dimension for large heterogeneous networks that have weakly distinguishable community structure. For a broad family o…
Selecting the number of components in PCA via random signflips
David Hong, Yue Sheng, Edgar Dobriban
Principal component analysis (PCA) is a foundational tool in modern data analysis, and a crucial step in PCA is selecting the number of components to keep. However, classical selec…
Generic Properties of Koopman Eigenfunctions for Stable Fixed Points and Periodic Orbits
Matthew D. Kvalheim, David Hong, Shai Revzen
Our recent work established existence and uniqueness results for globally defined linearizing semiconjugacies for flows having a gl…
Provable tradeoffs in adversarially robust classification
Edgar Dobriban, Hamed Hassani, David Hong +1
It is well known that machine learning methods can be vulnerable to adversarially-chosen perturbations of their inputs. Despite significant progress in the area, foundational open…
Baseline Estimation of Commercial Building HVAC Fan Power Using Tensor Completion
Shunbo Lei, David Hong, Johanna L. Mathieu +1
Commercial building heating, ventilation, and air conditioning (HVAC) systems have been studied for providing ancillary services to power grids via demand response (DR). One critic…
Stochastic Gradients for Large-Scale Tensor Decomposition
Tamara G. Kolda, David Hong
Tensor decomposition is a well-known tool for multiway data analysis. This work proposes using stochastic gradients for efficient generalized canonical polyadic (GCP) tensor decomp…
Generalized Canonical Polyadic Tensor Decomposition
David Hong, Tamara G. Kolda, Jed A. Duersch
Tensor decomposition is a fundamental unsupervised machine learning method in data science, with applications including network analysis and sensor data processing. This work devel…
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…