Graph Construction with Label Information for Semi-Supervised Learning
This work addresses a bottleneck in semi-supervised learning for researchers and practitioners by enhancing graph construction with label information, though it is incremental as it builds on existing methods like LRR.
The paper tackles the problem of graph-based semi-supervised learning by incorporating label information into graph construction, proposing Semi-Supervised Low-Rank Representation (SSLRR) which enforces zero edge weights between labeled samples of different classes. Experiment results show this method better captures global geometric structure and improves effectiveness for semi-supervised learning tasks.
In the literature, most existing graph-based semi-supervised learning (SSL) methods only use the label information of observed samples in the label propagation stage, while ignoring such valuable information when learning the graph. In this paper, we argue that it is beneficial to consider the label information in the graph learning stage. Specifically, by enforcing the weight of edges between labeled samples of different classes to be zero, we explicitly incorporate the label information into the state-of-the-art graph learning methods, such as the Low-Rank Representation (LRR), and propose a novel semi-supervised graph learning method called Semi-Supervised Low-Rank Representation (SSLRR). This results in a convex optimization problem with linear constraints, which can be solved by the linearized alternating direction method. Though we take LRR as an example, our proposed method is in fact very general and can be applied to any self-representation graph learning methods. Experiment results on both synthetic and real datasets demonstrate that the proposed graph learning method can better capture the global geometric structure of the data, and therefore is more effective for semi-supervised learning tasks.