Minimally Supervised Learning using Topological Projections in Self-Organizing Maps
CoRR(2024)
摘要
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 ultimately 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 semi-supervised model significantly outperforms
traditional regression techniques, including linear and polynomial regression,
Gaussian process regression, K-nearest neighbors, as well as various deep
neural network models.
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