Summary of Minimally Supervised Learning Using Topological Projections in Self-organizing Maps, by Zimeng Lyu et al.
Minimally Supervised Learning using Topological Projections in Self-Organizing Maps
by Zimeng Lyu, Alexander Ororbia, Rui Li, Travis Desell
First submitted to arxiv on: 12 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neural and Evolutionary Computing (cs.NE)
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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 introduces a semi-supervised learning approach based on topological projections in self-organizing maps (SOMs) to predict parameters with minimal labeled data. The method trains SOMs on unlabeled datasets and assigns available labeled data points to key best matching units (BMU). Newly-encountered data point values are estimated using the average of the n closest labeled data points in the SOM’s U-matrix, along with a topological shortest path distance calculation scheme. The proposed model outperforms traditional regression techniques, including linear and polynomial regression, Gaussian process regression, K-nearest neighbors, deep neural network models, and related clustering schemes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make better decisions by predicting important parameters without needing to spend too much time and money collecting data. Normally, we need lots of labeled data points to do this accurately. But the new method only needs a few labeled data points because it uses information from big datasets that aren’t labeled. It does this by using special maps called self-organizing maps (SOMs) and finding the closest labeled data points to new, unknown data points. The results show that this approach is better than other common methods for doing parameter prediction. |
Keywords
* Artificial intelligence * Clustering * Neural network * Regression * Semi supervised
