Kyle Caudle

South Dakota School of Mines and Technology

H-index: 6

North America-United States

About Kyle Caudle

Kyle Caudle, With an exceptional h-index of 6 and a recent h-index of 4 (since 2020), a distinguished researcher at South Dakota School of Mines and Technology, specializes in the field of non-parametric statistics, data streams, density estimation, spline smoothing.

His recent articles reflect a diverse array of research interests and contributions to the field:

Tensor discriminant analysis on grassmann manifold with application to video based human action recognition

Temporal Tensor Factorization for Multidimensional Forecasting

Deep Generative Modeling for Communication Systems Testing and Data Sharing

Session 4: Advancing Machine Learning Through Multilinear Subspace Methods

Anomaly Detection from Multilinear Observations via Time-Series Analysis and 3DTPCA

Forecasting Multilinear Data via Transform-Based Tensor Autoregression

High-Order Multilinear Discriminant Analysis via Order- Tensor Eigendecomposition

Kernelization of tensor discriminant analysis with application to image recognition

Kyle Caudle Information

University	South Dakota School of Mines and Technology
Position	___
Citations(all)	99
Citations(since 2020)	61
Cited By	59
hIndex(all)	6
hIndex(since 2020)	4
i10Index(all)	1
i10Index(since 2020)	0
Email	Access Email
University Profile Page	South Dakota School of Mines and Technology
Google Scholar	View Google Scholar Profile

Kyle Caudle Skills & Research Interests

non-parametric statistics

data streams

density estimation

spline smoothing

Top articles of Kyle Caudle

Title	Journal	Author(s)	Publication Date
Tensor discriminant analysis on grassmann manifold with application to video based human action recognition	International Journal of Machine Learning and Cybernetics	Cagri Ozdemir Randy C Hoover Kyle Caudle Karen Braman	2024/1/29
Temporal Tensor Factorization for Multidimensional Forecasting		Jackson Cates Karissa Scipke Randy Hoover Kyle Caudle	2023
Deep Generative Modeling for Communication Systems Testing and Data Sharing		Trevor Krason Kyle Caudle Randy Hoover Larry Pyeatt	2023
Session 4: Advancing Machine Learning Through Multilinear Subspace Methods		Cagri Ozdimir Randy C Hoover Kyle Caudle Karen Braman Jackson Cates	2023
Anomaly Detection from Multilinear Observations via Time-Series Analysis and 3DTPCA		Jackson Cates Randy C Hoover Kyle Caudle David Marchette Cagri Ozdemir	2022/12/12
Forecasting Multilinear Data via Transform-Based Tensor Autoregression	arXiv preprint arXiv:2205.12201	Jackson Cates Randy C Hoover Kyle Caudle Cagri Ozdemir Karen Braman ...	2022/5/24
High-Order Multilinear Discriminant Analysis via Order- Tensor Eigendecomposition	arXiv preprint arXiv:2205.09191	Cagri Ozdemir Randy C Hoover Kyle Caudle Karen Braman	2022/5/18
Kernelization of tensor discriminant analysis with application to image recognition		Cagri Ozdemir Randy C Hoover Kyle Caudle Karen Braman	2022/12/12
A new approach to multilinear dynamical systems and control	arXiv preprint arXiv:2108.13583	Randy C Hoover Kyle Caudle Karen Braman	2021/8/31
A review of flow field forecasting: A high‐dimensional forecasting procedure		Kyle Caudle Patrick S Fleming Randy C Hoover	2021/1
Fast tensor singular value decomposition using the low-resolution features of tensors		Cagri Ozdemir Randy C Hoover Kyle Caudle	2021/12/13
Transform-Based Tensor Auto Regression for Multilinear Time Series Forecasting		Jackson Cates Randy C Hoover Kyle Caudle Riley Kopp Cagri Ozdemir	2021/12/13
2DTPCA: A new framework for multilinear principal component analysis		Cagri Ozdemir Randy C Hoover Kyle Caudle	2021/9/19