Manifold regularization

Sat, 18 Jul 2026 10:06:42 +0000

Definition

Manifold regularization extends traditional regularization methods by incorporating the intrinsic geometry of the data distribution. It operates under the assumption that high-dimensional data points cluster along a lower-dimensional manifold. By minimizing a regularizer that penalizes functions varying rapidly along the manifold, the model leverages both labeled and unlabeled data. This approach improves generalization performance, particularly when labeled data is scarce, by ensuring smooth decision boundaries within the data’s natural structure.

Co-training

Sat, 18 Jul 2026 09:49:31 +0000

Definition

This method leverages multiple distinct feature sets (views) of the same data points. Initially, two classifiers are trained on small labeled datasets from each view. They then predict labels for unlabeled data, selecting high-confidence predictions to augment the training set of the other classifier. This iterative process expands the effective training data size, improving generalization when labeled data is scarce but abundant unlabeled data exists.

Summary

Co-training is a semi-supervised learning algorithm where two views of the data are used to train separate classifiers that iteratively label unlabeled data for each other.

Semi Supervised on English AI Terms Dictionary

Manifold regularization

Definition

Co-training

Definition

Summary