Large-Scale Semi-Supervised Learning via Graph Structure Learning over High-Dense Points
This addresses scalability and performance issues in semi-supervised learning for large datasets with few labels, representing an incremental improvement over existing methods.
The paper tackles the problem of suboptimal performance and high computational complexity in graph-based semi-supervised learning with limited labeled data by proposing a method that learns a small set of vertexes and a graph to characterize high-dense regions, achieving good classification performance with linear computational complexity, especially for extremely small numbers of labels.
We focus on developing a novel scalable graph-based semi-supervised learning (SSL) method for a small number of labeled data and a large amount of unlabeled data. Due to the lack of labeled data and the availability of large-scale unlabeled data, existing SSL methods usually encounter either suboptimal performance because of an improper graph or the high computational complexity of the large-scale optimization problem. In this paper, we propose to address both challenging problems by constructing a proper graph for graph-based SSL methods. Different from existing approaches, we simultaneously learn a small set of vertexes to characterize the high-dense regions of the input data and a graph to depict the relationships among these vertexes. A novel approach is then proposed to construct the graph of the input data from the learned graph of a small number of vertexes with some preferred properties. Without explicitly calculating the constructed graph of inputs, two transductive graph-based SSL approaches are presented with the computational complexity in linear with the number of input data. Extensive experiments on synthetic data and real datasets of varied sizes demonstrate that the proposed method is not only scalable for large-scale data, but also achieve good classification performance, especially for extremely small number of labels.