Linear Algebra

Practical Leverage-Based Sampling for Low-Rank Tensor Decomposition

B. W. Larsen and T. G. Kolda, SIAM J. Matrix Analysis and Applications, 2022

Sketching Matrix Least Squares via Leverage Scores Estimates

B. W. Larsen and T. G. Kolda, , 2022

XPCA: Extending PCA for a Combination of Discrete and Continuous Variables

C. Anderson-Bergman, T. G. Kolda and K. Kincher-Winoto, arXiv, 2018

Sparse Versus Scarce

Sometimes the term sparse is used to refer to a matrix that has a large fraction of missing entries, but the more typical usage of that term is to refer to a matrix that has a large fraction of zero entries. We instead recommend the term scarce for a large amount of missing data and discuss various scenarios.

Temporal Link Prediction using Matrix and Tensor Factorizations

D. M. Dunlavy, T. G. Kolda and E. Acar, ACM Transactions on Knowledge Discovery from Data, 2011

Generalized BadRank with Graduated Trust

T. G. Kolda and M. J. Procopio, Tech. Rep., Sandia National Laboratories, 2009

Resolving the Sign Ambiguity in the Singular Value Decomposition

R. Bro, E. Acar and T. G. Kolda, Journal of Chemometrics, 2008

Hidden Markov Models for Chromosome Identification

J. M. Conroy, J. R. L. Becker, W. Lefkowitz, K. L. Christopher, R. B. Surana, T. O’Leary, D. P. O’Leary and T. G. Kolda, In CBMS 2001: Proceedings of the 14th IEEE Symposium on Computer-Based Medical Systems, 2001

Chromosome Identification Using Hidden Markov Models: Comparison with Neural Networks, Singular Value Decomposition, Principal Components Analysis, and Fisher Discriminant Analysis

J. M. Conroy, T. G. Kolda, D. P. O’Leary and T. J. O’Leary, Laboratory Investigation, 2000

Algorithm 805: Computation and Uses of the Semidiscrete Matrix Decomposition

T. G. Kolda and D. P. O’Leary, ACM Transactions on Mathematical Software, 2000