Summary of Fast and Scalable Semi-supervised Learning For Multi-view Subspace Clustering, by Huaming Ling et al.
Fast and Scalable Semi-Supervised Learning for Multi-View Subspace Clustering
by Huaming Ling, Chenglong Bao, Jiebo Song, Zuoqiang Shi
First submitted to arxiv on: 11 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 presents a novel semi-supervised multi-view subspace clustering method called Fast and Scalable Semi-supervised Multi-view Subspace Clustering (FSSMSC), which addresses the high computational complexity issue in existing approaches. The FSSMSC method achieves linear computational and space complexity relative to the data size, using a consensus anchor graph across all views that represents each data point as a sparse linear combination of chosen landmarks. A unified optimization model is proposed to simultaneously learn both the anchor graph and label propagation process, solved using an effective alternating update algorithm with convergence guarantees. The method also deduces low-dimensional representations for raw data using the obtained anchor graph and landmarks’ low-dimensional representations, followed by straightforward clustering on these representations to achieve final clustering results. Extensive experiments on multiple benchmark datasets validate the effectiveness and efficiency of FSSMSC. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to group similar things together called Fast and Scalable Semi-supervised Multi-view Subspace Clustering (FSSMSC). This method is good at handling big data sets quickly and efficiently, which was a problem with previous methods. The FSSMSC method does this by creating a graph that shows how different pieces of information are related to each other. It then uses this graph to group similar things together. This makes it easier to find patterns in the data and identify groups that are similar. The authors tested their method on many different data sets and found that it worked well. |
Keywords
» Artificial intelligence » Clustering » Optimization » Semi supervised
