Semi-supervised Learning with Explicit Relationship Regularization
This work addresses the challenge of improving semi-supervised learning methods for researchers and practitioners by introducing a novel regularization approach, though it appears incremental as it builds on existing algorithms.
The paper tackles the problem of leveraging target space structure in semi-supervised learning by explicitly regularizing relationships between function evaluations, showing significant performance improvements in classification, clustering, and dimensionality reduction tasks.
In many learning tasks, the structure of the target space of a function holds rich information about the relationships between evaluations of functions on different data points. Existing approaches attempt to exploit this relationship information implicitly by enforcing smoothness on function evaluations only. However, what happens if we explicitly regularize the relationships between function evaluations? Inspired by homophily, we regularize based on a smooth relationship function, either defined from the data or with labels. In experiments, we demonstrate that this significantly improves the performance of state-of-the-art algorithms in semi-supervised classification and in spectral data embedding for constrained clustering and dimensionality reduction.