2018/03/07 by Kajsa Møllersen, Møllersen, Kajsa, Jon Yngve Hardeberg +3
Computer Science · Mathematics · #Advanced Image and Video Retrieval Techniques #FOS: Computer and information sciences #Image Retrieval and Classification Techniques #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Video Analysis and Summarization #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1803.02782
openalex publication_date 2018/03/07 · arxiv created 2018/10/12 · arxiv updated 2018/10/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In multi-instance (MI) learning, each object (bag) consists of multiple feature vectors (instances), and is most commonly regarded as a set of points in a multidimensional space. A different viewpoint is that the instances are realisations of random vectors with corresponding probability distribution, and that a bag is the distribution, not the realisations. In MI classification, each bag in the training set has a class label, but the instances are unlabelled. By introducing the probability distribution space to bag-level classification problems, dissimilarities between probability distributions (divergences) can be applied. The bag-to-bag Kullback-Leibler information is asymptotically the best classifier, but the typical sparseness of MI training sets is an obstacle. We introduce bag-to-class divergence to MI learning, emphasising the hierarchical nature of the random vectors that makes bags from the same class different. We propose two properties for bag-to-class divergences, and an additional property for sparse training sets.