# A Semi-Supervised Self-Organizing Map with Adaptive Local Thresholds

## TL;DR

This paper introduces ALTSS-SOM, a semi-supervised learning method based on Self-Organizing Maps that adaptively adjusts to local data variance, improving classification and clustering performance with robustness to parameter settings.

## Contribution

It proposes a novel ALTSS-SOM model that dynamically switches learning modes and automatically adapts to local data variance, enhancing semi-supervised clustering and classification.

## Key findings

- ALTSS-SOM outperforms other semi-supervised methods in classification tasks.
- ALTSS-SOM surpasses pure clustering methods when labels are absent.
- The method is less sensitive to parameter variations than previous approaches.

## Abstract

In the recent years, there is a growing interest in semi-supervised learning, since, in many learning tasks, there is a plentiful supply of unlabeled data, but insufficient labeled ones. Hence, Semi-Supervised learning models can benefit from both types of data to improve the obtained performance. Also, it is important to develop methods that are easy to parameterize in a way that is robust to the different characteristics of the data at hand. This article presents a new method based on Self-Organizing Map (SOM) for clustering and classification, called Adaptive Local Thresholds Semi-Supervised Self-Organizing Map (ALTSS-SOM). It can dynamically switch between two forms of learning at training time, according to the availability of labels, as in previous models, and can automatically adjust itself to the local variance observed in each data cluster. The results show that the ALTSS-SOM surpass the performance of other semi-supervised methods in terms of classification, and other pure clustering methods when there are no labels available, being also less sensitive than previous methods to the parameters values.