RACH-Space: Reconstructing Adaptive Convex Hull Space with applications in weak supervision

Woojoo Na, Abiy Tasissa

Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

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Keywords: Machine Learning, Weakly Supervised learning, Convex Hull Analysis, Geometry, Label Model, Ensemble Learning

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TL;DR: A novel ensemble learning model used to aggregate multiple weak supervision signals to classify unlabeled dataset

Abstract: We introduce RACH-Space, a novel classification method in ensemble learning. In particular, we show its applicability as a label model for weakly supervised learning. RACH-Space offers simplicity in implementation with minimal assumptions on the data or weak signals. The model is well suited for scenarios where fully labeled data is not available. Our method is built upon geometrical interpretation of the space spanned by weak signals. Our analysis of the high dimensional convex hull structure underlying general set of weak signals bridges geometry with machine learning. Empirical results also demonstrate that RACH-Space works well in practice and compares favorably to best existing label models for weakly supervised learning.

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Submission Number: 2699