  Label noise in training data can significantly degrade a model's
generalization performance for supervised learning tasks. Here we focus on the
problem that noisy labels are primarily mislabeled samples, which tend to be
concentrated near decision boundaries, rather than uniformly distributed, and
whose features should be equivocal. To address the problem, we propose an
ensemble learning method to correct noisy labels by exploiting the local
structures of feature manifolds. Different from typical ensemble strategies
that increase the prediction diversity among sub-models via certain loss terms,
our method trains sub-models on disjoint subsets, each being a union of the
nearest-neighbors of randomly selected seed samples on the data manifold. As a
result, each sub-model can learn a coarse representation of the data manifold
along with a corresponding graph. Moreover, only a limited number of sub-models
will be affected by locally-concentrated noisy labels. The constructed graphs
are used to suggest a series of label correction candidates, and accordingly,
our method derives label correction results by voting down inconsistent
suggestions. Our experiments on real-world noisy label datasets demonstrate the
superiority of the proposed method over existing state-of-the-arts.
