Recent years have seen a growing number of graph-based semisupervised learning methods. While the literature currently contains several of these methods, their relationships with one another and with other graph-based data analysis algorithms remain unclear. In this paper, we present a unified view of graph-based semi-supervised learning. Our framework unifies three important and seemingly unre...

We present a machine learning task, which we call bidirectional semi-supervised learning, where label-only samples are given as well as labeled and unlabeled samples. A label-only sample contains the label information of the sample but not the feature information. Then, we propose a simple and effective graph-based method for bidirectional semisupervised learning in multi-label classification. ...

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

The results of aerial scene classification can provide valuable information for urban planning and land monitoring. In this specific field, there are always a number object-level semantic classes in big remote-sensing pictures. Complex label-space makes it hard to detect all the targets perceive corresponding semantics typical scene, thereby weakening sensing ability. Even worse, preparation la...

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...

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...

The Scene Graph Generation (SGG) task aims to detect all the objects and their pairwise visual relationships in a given image. Although SGG has achieved remarkable progress over last few years, almost existing models follow same training paradigm: they treat both object predicate classification as single-label problem, ground-truths are one-hot target labels. However, this prevalent paradigm ov...