Summary of Multi-view Subspace Clustering Via An Adaptive Consensus Graph Filter, by Lai Wei et al.
Multi-view Subspace Clustering via An Adaptive Consensus Graph Filter
by Lai Wei, Shanshan Song
First submitted to arxiv on: 30 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel multiview subspace clustering (MVSC) method, which first assumes the existence of a consensus reconstruction coefficient matrix and then uses it to build a consensus graph filter. The filter is employed in each view for smoothing data and designing regularizers for the reconstruction coefficient matrices. The obtained matrices from different views are used to create constraints for the consensus reconstruction coefficient matrix. The interdependent components are optimized using an algorithm, which outperforms state-of-the-art methods on diverse multi-view datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a new way of grouping similar data points together in multiple datasets. They started by assuming there was a common pattern across all the views and used that to create a filter for each view. The filter helped smooth out any noise and created a regularized version of the data. By combining these regularized versions from all the views, they were able to get an even better understanding of the patterns in the data. |
Keywords
* Artificial intelligence * Clustering
