Summary of Semi-supervised Symmetric Non-negative Matrix Factorization with Low-rank Tensor Representation, by Yuheng Jia et al.
Semi-supervised Symmetric Non-negative Matrix Factorization with Low-Rank Tensor Representation
by Yuheng Jia, Jia-Nan Li, Wenhui Wu, Ran Wang
First submitted to arxiv on: 4 May 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 The proposed semi-supervised symmetric non-negative matrix factorization (SNMF) method utilizes available supervisory information to improve clustering ability. It introduces pairwise constraints from a local perspective, refining the similarity matrix and restraining vector distances in pairs. However, this overlooks the global perspective. To address this, a novel semi-supervised SNMF model is proposed, seeking low-rank representation for a tensor synthesized by the pairwise constraint matrix and an ideal similarity matrix. This strengthens these matrices simultaneously from a global perspective. The method iteratively boosts the similarity matrix and pairwise constraint matrix, leading to high-quality embeddings. The code is available on GitHub. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Semi-supervised symmetric non-negative matrix factorization (SNMF) helps group similar data points together. Current methods use limited information about how items are related, which isn’t ideal. To fix this, a new method is proposed that uses global information to improve SNMF clustering ability. This is done by seeking low-rank representation for a special tensor made from the relationships between items and their similarities. The method keeps refining its results until it achieves excellent grouping performance. |
Keywords
» Artificial intelligence » Clustering » Semi supervised
