Learning from Noisy Label Distributions (ICANN2017) Learning from Noisy Label Distributions (ICANN2017)

Yuya Yoshikawa, “Learning from Noisy Label Distributions,” The 26th International Conference on Artificial Neural Networks (ICANN), Sardinia, Italy, Sep. 2017.

In this paper, we consider a novel machine learning problem, that is, learning a classifier from noisy label distributions.
In this problem, each instance with a feature vector belongs to at least one group.
Then, instead of the true label of each instance, we observe the label distribution of the instances associated with a group, where the label distribution is distorted by an unknown noise.
Our goals are to (1) estimate the true label of each instance, and (2) learn a classifier that predicts the true label of a new instance.
We propose a probabilistic model that considers true label distributions of groups and parameters that represent the noise as hidden variables.
The model can be learned based on a variational Bayesian method.
In numerical experiments, we show that the proposed model outperforms existing methods in terms of the estimation of the true labels of instances.

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