Marginalized Denoising Autoencoders for Domain Adaptation (1206.4683v1)

Abstract: 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. In contrast to SDAs, our approach of mSDA marginalizes noise and thus does not require stochastic gradient descent or other optimization algorithms to learn parameters ? in fact, they are computed in closed-form. Consequently, mSDA, which can be implemented in only 20 lines of MATLAB^{TM}, significantly speeds up SDAs by two orders of magnitude. Furthermore, the representations learnt by mSDA are as effective as the traditional SDAs, attaining almost identical accuracies in benchmark tasks.

• The paper introduces mSDA, a method that employs closed-form linear denoisers to marginalize corruptions without iterative training.
• The paper achieves significant computational efficiency, reducing training time by up to 700x while scaling to high-dimensional data.
• The paper demonstrates competitive domain adaptation performance on benchmark sentiment analysis tasks by minimizing transfer loss.

Marginalized Denoising Autoencoders for Domain Adaptation

The paper "Marginalized Denoising Autoencoders for Domain Adaptation" by Minmin Chen, Zhixiang Xu, Kilian Q. Weinberger, and Fei Sha introduces a novel approach called marginalized Stacked Denoising Autoencoder (mSDA) aimed at enhancing the efficiency and scalability of the traditional Stacked Denoising Autoencoders (SDAs) in the context of domain adaptation. This research builds upon the foundational work in SDAs, presenting a method that not only achieves nearly identical classification performance but significantly reduces computational complexity.

SDAs have established effectiveness in learning robust feature representations for domain adaptation tasks through their ability to reconstruct original input data from corrupted versions. However, traditional SDAs suffer from high computational costs due to iterative training algorithms, such as stochastic gradient descent, which hinder scalability to high-dimensional data. The proposed mSDA addresses these limitations by introducing a method that allows for closed-form solutions, thereby circumventing the need for iterative parameter tuning.

Key Contributions

1. Linear Denoisers: The paper's primary innovation involves using linear denoisers as basic building blocks, enabling parameter optimization in closed form. This crucial shift allows mSDA to marginalize the corruptions analytically, effectively simulating training with an infinitely large number of corrupted inputs.
2. Computational Efficiency: The closed-form solution drastically reduces training times—from hours or days to mere minutes—making mSDA considerably more practical for large-scale applications. This is validated by a reported speedup of approximately 180x on smaller Amazon benchmark tasks and up to 700x on a larger set of 380 domain adaptation tasks.
3. High-Dimensional Data: The proposed approach can efficiently handle high-dimensional data, such as text represented by bag-of-words models with tens of thousands of features. This is achieved by dividing input features into random subspaces and learning independent linear mappings, then averaging the reconstructions.
4. Domain Adaptation Performance: Empirical results indicate that mSDA achieves competitive performance with traditional SDAs across standard benchmark datasets for domain adaptation, particularly in sentiment analysis tasks involving diverse product review domains. The learned features from mSDA help reduce the transfer loss significantly when compared to baseline methods and other state-of-the-art algorithms like Structural Correspondence Learning (SCL) and CODA.

Implications and Future Research

The practical implications of mSDA are substantial, offering a method that balances robustness in feature representation with computational feasibility. This equilibrium makes mSDA particularly relevant for applications involving large-scale and high-dimensional datasets such as text and image processing, which are ubiquitous in fields like natural language processing and computer vision.

In the theoretical domain, this work contributes a streamlined approach to leveraging the powerful feature learning capabilities of deep architectures while avoiding the complexities inherent in non-linear iterative training methods. This may inspire further research into other linear approximations or closed-form solutions applicable to various other neural architectures beyond autoencoders.

Future Developments

Moving forward, potential research avenues include:

• Extending mSDA to Other Architectures: Investigating how the marginalized corruption approach can be mapped onto other deep learning structures, such as convolutional neural networks (CNNs) or recurrent neural networks (RNNs).
• Hybrid Models: Combining the strengths of mSDA with non-linear transformations or embeddings to push the boundaries of feature representation and leverage the benefits of both linear and non-linear modeling.
• Domain-Specific Adaptations: Tailoring mSDA to optimize for specific tasks beyond sentiment analysis, such as image classification in varied lighting conditions or adaptation in speech recognition across different accents.

In conclusion, the proposed mSDA framework offers a compelling solution to the computational challenges posed by traditional SDAs, enhancing both the scalability and efficiency of domain adaptation processes. The combination of closed-form solutions with robust feature representation learning marks a significant stride in the development of practical deep learning models, with wide-ranging implications and potential for future research expansion.