Marginalized Stacked Denoising Autoencoders

Stacked Denoising Autoencoders (SDAs) [4] have been used successfully in many learning scenarios and application domains. In short, denoising autoencoders (DAs) train one-layer neural networks to reconstruct input data from partial random corruption. The denoisers are then stacked into deep learning architectures where the weights are fine-tuned with back-propagation. Alternatively, the outputs of intermediate layers can be used as input features to other learning algorithms. These learned feature representations are known to improve classification accuracies in many cases. For example, Glorot et. al. [3] applied SDAs to domain adaptation and demonstrated that these learned features , when used with a simple linear SVM classifier, yield record performance in benchmark sentiment analysis tasks [1].