Relational Similarity Machines
This addresses the need for adaptable relational learning tools for researchers and practitioners dealing with diverse classification problems and data constraints, though it appears incremental in improving existing methods.
The paper tackles the problem of relational learning methods being inefficient and inflexible for various settings, proposing Relational Similarity Machines (RSM) as a fast and flexible framework that demonstrates effectiveness across multiple tasks and data.
This paper proposes Relational Similarity Machines (RSM): a fast, accurate, and flexible relational learning framework for supervised and semi-supervised learning tasks. Despite the importance of relational learning, most existing methods are hard to adapt to different settings, due to issues with efficiency, scalability, accuracy, and flexibility for handling a wide variety of classification problems, data, constraints, and tasks. For instance, many existing methods perform poorly for multi-class classification problems, graphs that are sparsely labeled or network data with low relational autocorrelation. In contrast, the proposed relational learning framework is designed to be (i) fast for learning and inference at real-time interactive rates, and (ii) flexible for a variety of learning settings (multi-class problems), constraints (few labeled instances), and application domains. The experiments demonstrate the effectiveness of RSM for a variety of tasks and data.