Feature extraction for incomplete data via low-rank tensor decomposition with feature regularization
Multidimensional data (ie, tensors) with missing entries are common in practice. Extracting
features from incomplete tensors is an important yet challenging problem in many fields …
features from incomplete tensors is an important yet challenging problem in many fields …
Supervised learning for nonsequential data: A canonical polyadic decomposition approach
A Haliassos, K Konstantinidis… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Efficient modeling of feature interactions underpins supervised learning for nonsequential
tasks, characterized by a lack of inherent ordering of features (variables). The brute force …
tasks, characterized by a lack of inherent ordering of features (variables). The brute force …
Feature extraction for incomplete data via low-rank tucker decomposition
Extracting features from incomplete tensors is a challenging task which is not well explored.
Due to the data with missing entries, existing feature extraction methods are not applicable …
Due to the data with missing entries, existing feature extraction methods are not applicable …