RACH-Space: Reconstructing Adaptive Convex Hull Space with Applications in Weak Supervision

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 …

algorithm applications assumptions cs.lg data implementation information labelling labels math.oc practical simplicity space supervised learning supervision