RACH-Space: Reconstructing Adaptive Convex Hull Space with Applications in Weak Supervision
This addresses the practical challenge of labeling data when fully labeled datasets are unavailable, though it appears incremental as it builds on existing label models.
The paper tackles the problem of labeling unlabeled data in weakly supervised learning with incomplete, noisy information by introducing RACH-Space, an algorithm that reconstructs adaptive convex hull space. Empirical results show it works well in practice and compares favorably to existing label models.
We introduce RACH-Space, an algorithm for labelling unlabelled data in weakly supervised learning, given incomplete, noisy information about the labels. RACH-Space offers simplicity in implementation without requiring hard assumptions on data or the sources of weak supervision, and is well suited for practical applications where fully labelled data is not available. Our method is built upon a geometrical interpretation of the space spanned by the set of weak signals. We also analyze the theoretical properties underlying the relationship between the convex hulls in this space and the accuracy of our output labels, bridging geometry with machine learning. Empirical results demonstrate that RACH-Space works well in practice and compares favorably to the best existing label models for weakly supervised learning.