Unsupervised Representation Learning of High Dimensional Structured Data: A Comparative Study
Mashrin Srivastava · 2023
Unsupervised representation learning has emerged as a powerful technique for extracting meaningful features from high dimensional structured data, such as graphs, time series, text, and video. In this paper, we present a comprehensive comparative study of various unsupervised representation learning methods applied to different structured data modalities. We focus on evaluating the performance of these methods in terms of their ability to capture the underlying structure and relationships within the data, as well as their scalability and computational efficiency. Our analysis provides insights into the strengths and weaknesses of each method, and offers guidance for practitioners seeking to apply unsupervised representation learning techniques to their own structured data problems.