Abstract
In
partial label learning, each training example is assigned a set of candidate
labels, only one of which is the ground-truth label. Existing partial label
learning frameworks either assume each candidate label of equal confidence or
consider the ground-truth label as a latent variable hidden in the
indiscriminate candidate label set, while the different labeling confidence
levels of the candidate labels are regrettably ignored. In this paper, we
formalize the different labeling confidence levels as the latent label
distributions, and propose a novel unified framework to estimate the latent
label distributions while training the model simultaneously. Specifically, we
present a biconvex formulation with constrained local consistency and adopt an
alternating method to solve this optimization problem. The process of
alternating optimization exactly facilitates the mutual adaption of the model
training and the constrained label propagation. Extensive experimental results
on controlled UCI datasets as well as real-world datasets clearly show the
effectiveness of the proposed approach.
Keywords
- Machine Learning: Data Mining
- Machine Learning: Machine Learning
- Machine Learning: Semi-Supervised Learning
- Machine Learning: Multi-instance;Multi-label;Multi-view learning
