Summary of Probability-density-aware Semi-supervised Learning, by Shuyang Liu et al.
Probability-density-aware Semi-supervised Learning
by Shuyang Liu, Ruiqiu Zheng, Yunhang Shen, Ke Li, Xing Sun, Zhou Yu, Shaohui Lin
First submitted to arxiv on: 23 Dec 2024
Categories
- Main: Machine Learning (stat.ML)
- 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 investigates the role of probability density in semi-supervised learning (SSL), specifically focusing on the cluster assumption. Existing SSL methods often rely solely on similarity measures, overlooking this crucial aspect. The authors introduce a Probability-Density-Aware Measure (PM) to capture neighbor point similarities while considering the cluster assumption. They also develop PMLP, an algorithm that utilizes PM to propagate labels effectively. Interestingly, traditional pseudo-labeling can be viewed as a special case of PMLP, providing insights into its superior performance. The authors demonstrate the efficacy of PMLP through extensive experiments, outperforming recent methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Semi-supervised learning helps computers learn from labeled and unlabeled data. Researchers have been working on making this process more effective by understanding how similar points in the data are related. In this paper, scientists introduce a new way to measure similarity between points that takes into account not just how close they are but also what kind of patterns they follow. This approach helps computers learn better from both labeled and unlabeled data. The authors tested their method on various datasets and found it performed significantly better than other recent methods. |
Keywords
» Artificial intelligence » Probability » Semi supervised
