An Integrated Autoencoder-Based Filter for Sparse Big Data

TL;DR

This paper introduces an integrated autoencoder filter designed for sparse big data that leverages auxiliary information to improve accuracy and robustness, demonstrating superior performance on GPS trajectory data.

Contribution

The paper presents a novel autoencoder-based filter that effectively handles data sparsity by incorporating auxiliary information, balancing accuracy, speed, and complexity.

Findings

Outperforms state-of-the-art methods in accuracy and robustness

Effective in handling sparse GPS trajectory data

Balances prediction accuracy, convergence speed, and complexity

Abstract

We propose a novel filter for sparse big data, called an integrated autoencoder (IAE), which utilizes auxiliary information to mitigate data sparsity. The proposed model achieves an appropriate balance between prediction accuracy, convergence speed, and complexity. We implement experiments on a GPS trajectory dataset, and the results demonstrate that the IAE is more accurate and robust than some state-of-the-art methods.