Relational Autoencoder for Feature Extraction (1802.03145v1)

Abstract: Feature extraction becomes increasingly important as data grows high dimensional. Autoencoder as a neural network based feature extraction method achieves great success in generating abstract features of high dimensional data. However, it fails to consider the relationships of data samples which may affect experimental results of using original and new features. In this paper, we propose a Relation Autoencoder model considering both data features and their relationships. We also extend it to work with other major autoencoder models including Sparse Autoencoder, Denoising Autoencoder and Variational Autoencoder. The proposed relational autoencoder models are evaluated on a set of benchmark datasets and the experimental results show that considering data relationships can generate more robust features which achieve lower construction loss and then lower error rate in further classification compared to the other variants of autoencoders.

Relational Autoencoder for Feature Extraction

In the domain of machine learning, particularly in feature extraction for high-dimensional data, the use of autoencoders has achieved significant recognition. The paper "Relational Autoencoder for Feature Extraction" introduces a novel approach that incorporates data relationships, along with the data features themselves, into the framework of the autoencoder. This relational perspective aims to enhance the robustness and precision of the features extracted.

Key Elements and Methodology

The core innovation of the Relational Autoencoder (RAE) involves augmenting the autoencoder architecture to account not only for the reconstruction of individual data samples but also for maintaining the intrinsic relationships between data samples. The central hypothesis is that relationships among data points, if preserved, can lead to more meaningful high-level feature representations. This expanded attention to relationships hopes to bridge the gap where traditional autoencoders may falter, specifically in capturing the dependencies and proximities inherent in the dataset.

The RAE achieves this by introducing a dual-component objective function. One component minimizes the discrepancy between the reconstructed and original data samples, while the other minimizes the loss of relationships among these samples. A scaling parameter, $\alpha$, is crucial in balancing these two components. It facilitates flexibility, allowing the model to tailor its focus between pure reconstruction and relationship maintenance depending on the characteristics of the data.

Extension to Other Autoencoder Variants

Further extending the RAE model, the authors propose relational extensions to various well-established autoencoder variants, including the Sparse Autoencoder (SAE), Denoising Autoencoder (DAE), and Variational Autoencoder (VAE). Each variant is adapted to consider data relationships through tailored modifications of their respective objective functions.

For example, the Relational Sparse Autoencoder (RSAE) maintains low neuron weights by including a regularization term, while the Relational Denoising Autoencoder (RDAE) learns to reconstruct input corrupted by additive isotropic Gaussian noise. This corruption process aids in deriving robust features. The Relational Variational Autoencoder (RVAE), on the other hand, extends the variational framework by preserving relationships through tweaking the Kullback-Leibler divergence minimization process.

Experimental Findings

Empirical evaluations were conducted using the MNIST and CIFAR-10 benchmarks. These experiments demonstrate the superiority of the RAE in reducing reconstruction loss, as compared to both basic autoencoders and the Generative Autoencoder (GAE). The experiments reveal a marked improvement in generating robust features that are significantly less prone to error in subsequent classification tasks. The relational extensions of existing autoencoder methodologies also exhibit an impressive performance uplift, underlining the potential benefits of relationship-inclusive feature extraction.

The successful performance of RAEs across these datasets suggests their applicability to complex domains such as image and video processing, where preserving underlying sample relationships during feature extraction can be critically important.

Implications and Future Directions

The exploration of relationship-aware feature extraction holds several theoretical and practical implications. By improving upon the traditional autoencoder framework, the RAE model highlights the necessity and potential of considering intrinsic data relationships. This advocation opens avenues for further research into relationship-preserving techniques across diverse neural network architectures and data types.

Future exploration may consider optimizing computational efficiency given the added complexity of calculating and preserving relationships. Additionally, refining the methods to automatically determine the optimal balance of $\alpha$ for varying datasets could enhance the model's adaptability without additional manual intervention.

The capabilities demonstrated by relational autoencoders invite further dialogue on their integration within broader AI systems and workflows, potentially leading to advancements in tasks requiring nuanced understanding and processing of complex, high-dimensional datasets.