A Unifying Generative Model for Graph Learning Algorithms: Label Propagation, Convolutions, and Combinations

Conventional graph-based semi-supervised learning methods predominantly focus on single label problem. However, it is more popular in real-world applications that an example is associated with multiple labels simultaneously. In this paper, we propose a novel graph-based learning framework in the setting of semi-supervised learning with multiple labels. This framework is characterized by simulta...

Graph classification is an important data mining task, and various graph kernel methods have been proposed recently for this task. These methods have proven to be effective, but they tend to have high computational overhead. In this paper, we propose an alternative approach to graph classification that is based on feature vectors constructed from different global topological attributes, as well...

Accurate and consistent labeling of longitudinal cortical surfaces is essential to understand the early dynamic development of cortical structure and function in both normal and abnormal infant brains. In this paper, we propose a novel method for simultaneous, consistent, and unbiased labeling of longitudinal dynamic cortical surfaces in the infant brain MR images. The proposed method is formul...

Multi-label classification has gained significant attention during recent years, due to the increasing number of modern applications associated with multi-label data. Despite its short life, different approaches have been presented to solve the task of multi-label classification. LIFT is a multi-label classifier which utilizes a new strategy to multi-label learning by leveraging label-specific ...

Multi-label classification is an important task in many modern machine learning applications. Accurate methods model the correlations and relationships between labels, either by assuming a low-dimensional embedding of the labels or a graph structure of label dependencies. While such interactions can be achieved using feed-forward predictors, problems with tight coupling between labels are bette...