Tong Wu

Orcid: 0000-0002-8935-1629

Affiliations:
  • Beijing Institute for General Artificial Intelligence, China
  • Rutgers University, Piscataway, NJ, USA (PhD 2017)


According to our database1, Tong Wu authored at least 13 papers between 2014 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Bibliography

2024
Online Tensor Max-Norm Regularization via Stochastic Optimization.
Trans. Mach. Learn. Res., 2024

2023
Online Tensor Low-Rank Representation for Streaming Data Clustering.
IEEE Trans. Circuits Syst. Video Technol., February, 2023

2020
Graph regularized low-rank representation for submodule clustering.
Pattern Recognit., 2020

Online Tensor Low-Rank Representation for Streaming Data.
Proceedings of the 30th IEEE International Workshop on Machine Learning for Signal Processing, 2020

2019
Clustering-Aware Structure-Constrained Low-Rank Submodule Clustering.
Proceedings of the 53rd Asilomar Conference on Signals, Systems, and Computers, 2019

2018
A Low Tensor-Rank Representation Approach for Clustering of Imaging Data.
IEEE Signal Process. Lett., 2018

2016
Human Action Attribute Learning From Video Data Using Low-Rank Representations.
CoRR, 2016

Clustering-aware structure-constrained low-rank representation model for learning human action attributes.
Proceedings of the IEEE 12th Image, Video, and Multidimensional Signal Processing Workshop, 2016

2015
Learning the Nonlinear Geometry of High-Dimensional Data: Models and Algorithms.
IEEE Trans. Signal Process., 2015

Hierarchical Union-of-Subspaces Model for Human Activity Summarization.
Proceedings of the 2015 IEEE International Conference on Computer Vision Workshop, 2015

Metric-Constrained Kernel Union of Subspaces.
Proceedings of the 2015 IEEE International Conference on Acoustics, 2015

2014
Subspace detection in a kernel space: The missing data case.
Proceedings of the IEEE Workshop on Statistical Signal Processing, 2014

Revisiting robustness of the union-of-subspaces model for data-adaptive learning of nonlinear signal models.
Proceedings of the IEEE International Conference on Acoustics, 2014


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