Metric learning approach for graph-based label propagation
This addresses a specific bottleneck in semi-supervised learning for practitioners, but it is incremental as it builds on existing graph-based methods.
The paper tackles the problem of inefficient graph-based semi-supervised learning due to suboptimal metrics, proposing an algorithm to learn a better vectorial representation for graph construction, resulting in improved task efficiency.
The efficiency of graph-based semi-supervised algorithms depends on the graph of instances on which they are applied. The instances are often in a vectorial form before a graph linking them is built. The construction of the graph relies on a metric over the vectorial space that help define the weight of the connection between entities. The classic choice for this metric is usually a distance measure or a similarity measure based on the euclidean norm. We claim that in some cases the euclidean norm on the initial vectorial space might not be the more appropriate to solve the task efficiently. We propose an algorithm that aims at learning the most appropriate vectorial representation for building a graph on which the task at hand is solved efficiently.