Extreme learning machine for classification and clustering on multiview data - Chen, Jichao (Nanyang Technological University, 2021)
Many real-world applications deal with data that have multiple feature sets. Such data is called multiview data, where each feature set is considered as a view. Many machine learning tasks have their multiview versions, which have similar objectives but dealing with multiview data. For example, the most general classification and clustering tasks have their multiview versions. Multiview classification aims to categorize multiview data into predefined classes. It is quite useful when dealing with complicated objects presented in different formats (e.g., 3D models). Multiview clustering separates the multiview data into clusters by considering their pairwise similarities in all views. It is widely used in feature learning and data labeling. Multiview spectral clustering is one of the most popular kinds of multiview clustering algorithms, which leverages eigendecomposition on the graph Laplacian to find the clusters. Meanwhile, extreme learning machine (ELM) is attracting more and more attention for its promising performance in various real-world applications. Therefore, the thesis investigates ELM for classification and clustering on multiview data.