Label-denoising Auto-Encoder for Classification with Inaccurate Supervision Information

Label noise is not uncommon in machine learning applications nowadays and imposes great challenges for many existing classifiers. In this paper we propose a new type of auto-encoder coined label-denoising auto-encoder to learn a representation for robust classification under this situation. For this purpose, we include both the feature and the (noisy) label of a data point in the input layer of the auto-encoder network, and during each learning iteration, we disturb the label according to the posterior probability of the data estimated by a softmax regression classifier. The learnt representation is shown to be robust against label noise on three real-world data-sets.