Loss Functions for Clustering in Multi-instance Learning
Abstrakt
Multi-instance learning belongs to one of recently fast developing areas of machine learning. It is a supervised learning method and this paper reports research into its unsupervised counterpart, multi-instance clustering. Whereas traditional clustering clusters points, multiinstance clustering clusters bags, i.e. multisets of points or of other kinds of objects. The paper focuses on the problem of loss functions for clustering. Three sophisticated loss functions used for clustering of points, contrastive predictive coding, triplet loss and magnet loss, are elaborated for multi-instance clustering. Finally, they are compared on 18 benchmark datasets, as well as on a real-world dataset.