Single-positive Multi-label Learning with Label Cardinality

TMLR Paper4809 Authors

Abstract: We study learning a multi-label classifier from partially labeled data, where each instance has only a single positive label. We explain how auxiliary information available on the label cardinality, the number of positive labels per instance, can be used for improving such methods. We consider auxiliary information of varying granularity, ranging from knowing just the maximum number of labels over all instances to knowledge on the distribution of label cardinalities and even the exact cardinality of each instance. We introduce methods leveraging the different types of auxiliary information, study how close to the fully labeled accuracy we can get under different scenarios, and show that a simple method only assuming the knowledge of the maximum cardinality is comparable to the state-of-the-art methods.

Submission Length: Regular submission (no more than 12 pages of main content)

Assigned Action Editor: ~Tongliang_Liu1

Submission Number: 4809