ECE PhD Dissertation Defense: Lichen Wang

PhD Dissertation Defense: Correlation Discovery for Multi-view and Multi-label Learning

Lichen Wang

Location: Zoom Link

Abstract: Correlation indicates the interactions or connections across different instances. It exists in a wide range of real-world applications such as social network, scene understanding, and time-series data analysis. Correlation provides the unique and informative knowledge to reveal the connections across instances, and it plays an essential and important role in machine learning field. However, recovering and utilizing correlation is challenging. First, it is hard to explicitly define and understand the correlations. Second, there are not sufficient datasets which contain the well-labeled task-specific correlations. Third, how to efficiently utilize the learned correlations for other down-stream tasks have not been well-explored.
In this dissertation research, we investigate the techniques to effectively discover various kinds of correlations in machine learning tasks including multi-view learning, multi-label learning, image/scene understanding, time-series data analysis, human action recognition, and graph representation learning. Specifically, we propose algorithms from the following perspectives: (1) designing an advanced correlation discovery network to automatically explore the label correlations in multi-label scenarios, (2) proposing a multi-view fusion strategy which effectively dig the latent correlations across different views, (3) exploring the correlations and structural knowledge from graph structured objects in an inductive and unsupervised scenario. To demonstrate the effectiveness of the proposed algorithms, various experiments on commonly used datasets have been implemented and the results shows the superiority of our algorithms over the other state-of-the-art methods.