Robust Corrupted Data Recovery and Clustering via Generalized Transformed Tensor Low-Rank Representation.
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2022.3215983
Abstract: Tensor analysis has received widespread attention in high-dimensional data learning. Unfortunately, the tensor data are often accompanied by arbitrary signal corruptions, including missing entries and sparse noise. How to recover the characteristics of the corrupted… read more here.
Keywords: tensor; low rank; generalized transformed; transformed tensor ... See more keywords
Learning Mixtures of Separable Dictionaries for Tensor Data: Analysis and Algorithms
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Transactions on Signal Processing"
DOI: 10.1109/tsp.2019.2952046
Abstract: This work addresses the problem of learning sparse representations of tensor data using structured dictionary learning. It proposes learning a mixture of separable dictionaries to better capture the structure of tensor data by generalizing the… read more here.
Keywords: mixtures separable; tensor data; learning mixture; separable dictionaries ... See more keywords
Guaranteed Tensor Recovery Fused Low-rankness and Smoothness
Sign Up to like & getrecommendations! Published in 2023 at "IEEE transactions on pattern analysis and machine intelligence"
DOI: 10.48550/arxiv.2302.02155
Abstract: Vast visual data like multi-spectral images and multi-frame videos are essentially with the tensor format. However, due to the defects of signal acquisition equipments, the practically collected tensor data are always with evident degradations like… read more here.
Keywords: tensor; tensor recovery; low rankness; tensor data ... See more keywords