Marginalized Denoising Autoencoders for Domain Adaptation

Understand

Stacked denoising autoencoders (SDAs) have been successfully used to learn new representations for domain adaptation.

• Recently, they have attained record accuracy on standard benchmark tasks of sentiment analysis across different text domains.
• SDAs learn robust data representations by reconstruction, recovering original features from data that are artificially corrupted with noise.
• In this paper, we propose marginalized SDA (mSDA) that addresses two crucial limitations of SDAs: high computational cost and lack of scalability to high-dimensional features.