Multiple instance learning with bag dissimilarities
| Autor: | Marco Loog, Veronika Cheplygina, David M. J. Tax |
|---|---|
| Přispěvatelé: | Medical Image Analysis, Center for Analysis, Scientific Computing & Appl. |
| Rok vydání: | 2015 |
| Předmět: |
FOS: Computer and information sciences
Image classification Drug activity prediction Machine Learning (stat.ML) Dissimilarity representation Machine learning computer.software_genre Machine Learning (cs.LG) Set (abstract data type) Statistics - Machine Learning Artificial Intelligence Similarity (psychology) Feature (machine learning) Representation (mathematics) Mathematics Contextual image classification business.industry Multiple instance learning Supervised learning Point set distance Computer Science - Learning Range (mathematics) ComputingMethodologies_PATTERNRECOGNITION Signal Processing Text categorization Computer Vision and Pattern Recognition Artificial intelligence Focus (optics) business computer Software |
| Zdroj: | Pattern Recognition, 48(1), 264-275. Elsevier |
| ISSN: | 0031-3203 |
| DOI: | 10.1016/j.patcog.2014.07.022 |
| Popis: | Multiple instance learning (MIL) is concerned with learning from sets (bags) of objects (instances), where the individual instance labels are ambiguous. In this setting, supervised learning cannot be applied directly. Often, specialized MIL methods learn by making additional assumptions about the relationship of the bag labels and instance labels. Such assumptions may fit a particular dataset, but do not generalize to the whole range of MIL problems. Other MIL methods shift the focus of assumptions from the labels to the overall (dis)similarity of bags, and therefore learn from bags directly. We propose to represent each bag by a vector of its dissimilarities to other bags in the training set, and treat these dissimilarities as a feature representation. We show several alternatives to define a dissimilarity between bags and discuss which definitions are more suitable for particular MIL problems. The experimental results show that the proposed approach is computationally inexpensive, yet very competitive with state-of-the-art algorithms on a wide range of MIL datasets. Pattern Recognition, in press |
| Databáze: | OpenAIRE |
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