Onion Universe Algorithm: Applications in Weakly Supervised Learning
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We introduce Onion Universe Algorithm (OUA), a novel classification method in ensemble learning. In particular, we show its applicability as a label model for weakly supervised learning. OUA offers simplicity in implementation, computational efficiency, and does not rely on any assumptions regarding 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. Empirical results support our analysis of the hidden geometric structure underlying general set of weak signals and also illustrates that OUA works well in practice. We show empirical evidence that OUA performs favorably on common benchmark datasets compared to existing label models for weakly supervised learning.
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