activity
20182022
most citedMultidimensional Scaling: Infinite Metric Measure Spaces

3 citations · 4 across the 3 of their papers we have counts for

collaborators

7 papers

cond-mat.mtrl-sci2022

TopTemp: Parsing Precipitate Structure from Temper Topology

Lara Kassab, Scott Howland, Henry Kvinge +2

Technological advances are in part enabled by the development of novel manufacturing processes that give rise to new materials or material property improvements. Development and ev…

cs.IR20221 cited

Semi-supervised Nonnegative Matrix Factorization for Document Classification

Jamie Haddock, Lara Kassab, Sixian Li +9

We propose new semi-supervised nonnegative matrix factorization (SSNMF) models for document classification and provide motivation for these models as maximum likelihood estimators.…

cs.LG2020

Semi-supervised NMF Models for Topic Modeling in Learning Tasks

Jamie Haddock, Lara Kassab, Sixian Li +9

We propose several new models for semi-supervised nonnegative matrix factorization (SSNMF) and provide motivation for SSNMF models as maximum likelihood estimators given specific d…

cs.LG2020

On Large-Scale Dynamic Topic Modeling with Nonnegative CP Tensor Decomposition

Miju Ahn, Nicole Eikmeier, Jamie Haddock +7

There is currently an unprecedented demand for large-scale temporal data analysis due to the explosive growth of data. Dynamic topic modeling has been widely used in social and dat…

math.ST2019

Multidimensional Scaling on Metric Measure Spaces

Henry Adams, Mark Blumstein, Lara Kassab

Multidimensional scaling (MDS) is a popular technique for mapping a finite metric space into a low-dimensional Euclidean space in a way that best preserves pairwise distances. We o…

math.ST20193 cited

Multidimensional Scaling: Infinite Metric Measure Spaces

Lara Kassab

Multidimensional scaling (MDS) is a popular technique for mapping a finite metric space into a low-dimensional Euclidean space in a way that best preserves pairwise distances. We s…