Knowledge Elicitation using Deep Metric Learning and Psychometric Testing

• URL: http://arxiv.org/abs/2004.06353v1
• Date: Tue, 14 Apr 2020 08:33:42 GMT
• Title: Knowledge Elicitation using Deep Metric Learning and Psychometric Testing
• Authors: Lu Yin, Vlado Menkovski, Mykola Pechenizkiy
• Abstract summary: We provide a method for efficient hierarchical knowledge elicitation from experts working with high-dimensional data such as images or videos. The developed models embed the high-dimensional data in a metric space where distances are semantically meaningful, and the data can be organized in a hierarchical structure.
• Score: 15.989397781243225
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knowledge present in a domain is well expressed as relationships between corresponding concepts. For example, in zoology, animal species form complex hierarchies; in genomics, the different (parts of) molecules are organized in groups and subgroups based on their functions; plants, molecules, and astronomical objects all form complex taxonomies. Nevertheless, when applying supervised machine learning (ML) in such domains, we commonly reduce the complex and rich knowledge to a fixed set of labels, and induce a model shows good generalization performance with respect to these labels. The main reason for such a reductionist approach is the difficulty in eliciting the domain knowledge from the experts. Developing a label structure with sufficient fidelity and providing comprehensive multi-label annotation can be exceedingly labor-intensive in many real-world applications. In this paper, we provide a method for efficient hierarchical knowledge elicitation (HKE) from experts working with high-dimensional data such as images or videos. Our method is based on psychometric testing and active deep metric learning. The developed models embed the high-dimensional data in a metric space where distances are semantically meaningful, and the data can be organized in a hierarchical structure. We provide empirical evidence with a series of experiments on a synthetically generated dataset of simple shapes, and Cifar 10 and Fashion-MNIST benchmarks that our method is indeed successful in uncovering hierarchical structures.

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