A Probabilistic Bag-to-Class Approach to Multiple-Instance Learning

Kajsa Møllersen, Jon Yngve Hardeberg and Fred Godtliebsen
Additional contact information
Kajsa Møllersen: Department of Community Medicine, Faculty of Health Science, UiT The Arctic University of Norway, N-9037 Tromsø, Norway
Jon Yngve Hardeberg: Department of Computer Science, Faculty of Information Technology and Electrical Engineering, NTNU—Norwegian University of Science and Technology, N-2815 Gjøvik, Norway
Fred Godtliebsen: Department of Mathematics and Statistics, Faculty of Science and Technology, UiT The Arctic University of Norway, N-9037 Tromsø, Norway

Data, 2020, vol. 5, issue 2, 1-25

Abstract: Multi-instance (MI) learning is a branch of machine learning, where each object (bag) consists of multiple feature vectors (instances)—for example, an image consisting of multiple patches and their corresponding feature vectors. In MI classification, each bag in the training set has a class label, but the instances are unlabeled. The instances are most commonly regarded as a set of points in a multi-dimensional space. Alternatively, instances are viewed as realizations of random vectors with corresponding probability distribution, where the bag is the distribution, not the realizations. 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, emphasizing 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, and propose a dissimilarity measure that fulfils them. Its performance is demonstrated on synthetic and real data. The probability distribution space is valid for MI learning, both for the theoretical analysis and applications.

Keywords: image classification; multi-instance learning; divergence; dissimilarity; bag-to-class; Kullback–Leibler (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2306-5729/5/2/56/pdf (application/pdf)
https://www.mdpi.com/2306-5729/5/2/56/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jdataj:v:5:y:2020:i:2:p:56-:d:376529

Access Statistics for this article

Data is currently edited by Ms. Cecilia Yang

More articles in Data from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().