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Found 4 papers, 2 papers with code
Persistent Laplacian-enhanced Algorithm for Scarcely Labeled Data Classification
Overall, it is a very efficient procedure that requires much less labeled data to perform well compared to many ML techniques, and it can be adapted for both small and large datasets.
Integrating Transformer and Autoencoder Techniques with Spectral Graph Algorithms for the Prediction of Scarcely Labeled Molecular Data
In molecular and biological sciences, experiments are expensive, time-consuming, and often subject to ethical constraints.
Multiscale Laplacian Learning
In particular, the performance of machine learning methods is often severely affected in case of diverse data, usually associated with smaller data sets or data related to areas of study where the size of the data sets is constrained by the complexity and/or high cost of experiments.
Multiclass Data Segmentation using Diffuse Interface Methods on Graphs
We present two graph-based algorithms for multiclass segmentation of high-dimensional data.
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