The Peaking Phenomenon in Semi-supervised Learning
This work addresses a counterintuitive issue in semi-supervised learning, which is incremental as it extends known supervised learning phenomena to a new setting.
The paper investigates the peaking phenomenon in semi-supervised learning, showing that adding unlabeled data can cause a more pronounced initial increase in error rate before improvement, as demonstrated through simulations and approximations.
For the supervised least squares classifier, when the number of training objects is smaller than the dimensionality of the data, adding more data to the training set may first increase the error rate before decreasing it. This, possibly counterintuitive, phenomenon is known as peaking. In this work, we observe that a similar but more pronounced version of this phenomenon also occurs in the semi-supervised setting, where instead of labeled objects, unlabeled objects are added to the training set. We explain why the learning curve has a more steep incline and a more gradual decline in this setting through simulation studies and by applying an approximation of the learning curve based on the work by Raudys & Duin.