Adaptive Weighted Low-Rank Sparse Representation for Multi-View Clustering
- Resource Type
- Periodical
- Authors
- Khan, M.A.; Khan, G.A.; Khan, J.; Anwar, T.; Ashraf, Z.; Atoum, I.A.; Ahmad, N.; Shahid, M.; Ishrat, M.; Alghamdi, A.A.
- Source
- IEEE Access Access, IEEE. 11:60681-60692 2023
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Geoscience
Nuclear Engineering
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Sparse matrices
Linear programming
Computer science
Symmetric matrices
STEM
Data mining
Weight control
Low-rank representation
spectral clustering
weighted multi-view data
sparse constraints
- Language
- ISSN
- 2169-3536
Ongoing researches on multiple view data are showing competitive behavior in the machine learning field. Multi-view clustering has gained widespread acceptance for managing multi-view data and improves clustering efficiency. Large dimensionality in data from various views has recently drawn a lot of interest from researchers. How to efficiently learns the appropriate lower dimensional subspace which can manage the valuable information from the diverse views is challenging and considerable issue. To concentrate on the mentioned issue, we asserted a novel clustering approach for multiple view data through low-rank representation. We consider the importance of each view by assigning the weight control factor. We combine consensus representation with the degree of disagreement among lower rank matrices. The single objective function unifies all factors. Furthermore, we give the efficient solution to update the variable and to optimized the objective function through the Augmented Lagrange’s Multiplier strategy. Real-world datasets are utilized in this study to exemplify the efficiency of the introduced technique, and it is contemplated to preceding algorithms to demonstrate its superiority.