# Minimally Supervised Learning using Topological Projections in Self-Organizing Maps

arXiv: https://arxiv.org/abs/2401.06923 · HTML: https://mlsanity.com/paper/2401.06923/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 48 references that are in the map.
- First posted on arXiv 12 Jan 2024.

## Builds on (most cited first, 9 of 9)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [Conditional Neural Processes](https://mlsanity.com/paper/1807.01613/) (2018)
- [FocalMix: Semi-Supervised Learning for 3D Medical Image Detection](https://mlsanity.com/paper/2003.09108/) (2020)
- [Semi-supervised Learning for Few-shot Image-to-Image Translation](https://mlsanity.com/paper/2003.13853/) (2020)
- [Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions](https://mlsanity.com/paper/1710.10304/) (2017)
- [Minimally-Supervised Structure-Rich Text Categorization via Learning on Text-Rich Networks](https://mlsanity.com/paper/2102.11479/) (2021)
- [Minimally-Supervised Speech Synthesis with Conditional Diffusion Model and Language Model: A Comparative Study of Semantic Coding](https://mlsanity.com/paper/2307.15484/) (2023)

## Built on by (most cited first, 2 of 2)

- [Neuro-mimetic Task-free Unsupervised Online Learning with Continual Self-Organizing Maps](https://mlsanity.com/paper/2402.12465/) (2024)
- [Minimally Supervised Topological Projections of Self-Organizing Maps for Phase of Flight Identification](https://mlsanity.com/paper/2402.11185/) (2024)

## Abstract (arXiv)

Parameter prediction is essential for many applications, facilitating insightful interpretation and decision-making. However, in many real life domains, such as power systems, medicine, and engineering, it can be very expensive to acquire ground truth labels for certain datasets as they may require extensive and expensive laboratory testing. In this work, we introduce a semi-supervised learning approach based on topological projections in self-organizing maps (SOMs), which significantly reduces the required number of labeled data points to perform parameter prediction, effectively exploiting information contained in large unlabeled datasets. Our proposed method first trains SOMs on unlabeled data and then a minimal number of available labeled data points are assigned to key best matching units (BMU). The values estimated for newly-encountered data points are computed utilizing the average of the n closest labeled data points in the SOM's U-matrix in tandem with a topological shortest path distance calculation scheme. Our results indicate that the proposed minimally supervised model significantly outperforms traditional regression techniques, including linear and polynomial regression, Gaussian process regression, K-nearest neighbors, as well as deep neural network models and related clustering schemes.