Robust tensor-on-tensor regression for multidimensional data modeling
In recent years, high-dimensional data, such as waveform signals and images have become
ubiquitous. This type of data is often represented by multiway arrays or tensors. Several …
ubiquitous. This type of data is often represented by multiway arrays or tensors. Several …
Federated generalized scalar-on-tensor regression
Complex systems are generating more and more high-dimensional data for which tensor
analysis showed promising results by capturing complex correlation structures of data. Such …
analysis showed promising results by capturing complex correlation structures of data. Such …
Federated Multiple Tensor-on-Tensor Regression (FedMTOT) for Multimodal Data under Data-Sharing Constraints
In recent years, diversified measurements reflect the system dynamics from a more
comprehensive perspective in system modeling and analysis, such as scalars, waveform …
comprehensive perspective in system modeling and analysis, such as scalars, waveform …
Noise‐Augmented Regularization of Tensor Regression With Tucker Decomposition
Tensor data are multi‐dimensional arrays. Low‐rank decomposition‐based regression
methods with tensor predictors exploit the structural information in tensor predictors while …
methods with tensor predictors exploit the structural information in tensor predictors while …
Applications of Modern Machine Learning Approaches to Address Real World Problems
T Yan - 2024 - search.proquest.com
In an era with increasingly available complex real-world data, various quantitative methods
have been developed to extract valuable insights from the data. Machine learning (ML) …
have been developed to extract valuable insights from the data. Machine learning (ML) …