303 citations · 885 across the 16 of their papers we have counts for
5 papers · 1 filter
Efficient Tensor Robust PCA under Hybrid Model of Tucker and Tensor Train
Yuning Qiu, Guoxu Zhou, Zhenhao Huang +2
Tensor robust principal component analysis (TRPCA) is a fundamental model in machine learning and computer vision. Recently, tensor train (TT) decomposition has been verified effec…
FedParking: A Federated Learning based Parking Space Estimation with Parked Vehicle assisted Edge Computing
Xumin Huang, Peichun Li, Rong Yu +3
As a distributed learning approach, federated learning trains a shared learning model over distributed datasets while preserving the training data privacy. We extend the applicatio…
Domain Confusion with Self Ensembling for Unsupervised Adaptation
Jiawei Wang, Zhaoshui He, Chengjian Feng +4
Data collection and annotation are time-consuming in machine learning, expecially for large scale problem. A common approach for this problem is to transfer knowledge from a relate…
Beyond Unfolding: Exact Recovery of Latent Convex Tensor Decomposition under Reshuffling
Chao Li, Mohammad Emtiyaz Khan, Zhun Sun +4
Exact recovery of tensor decomposition (TD) methods is a desirable property in both unsupervised learning and scientific data analysis. The numerical defects of TD methods, however…
Efficient Nonnegative Tucker Decompositions: Algorithms and Uniqueness
Guoxu Zhou, Andrzej Cichocki, Qibin Zhao +1
Nonnegative Tucker decomposition (NTD) is a powerful tool for the extraction of nonnegative parts-based and physically meaningful latent components from high-dimensional tensor dat…