Abstract
Descriptions of images form the backbone for many intelligent systems, assuming descriptions that randomly vary in construction and content, but where description content is homogeneous. This assumption becomes problematic being extended to descriptions of images of people [14], where people are known to show systematic biases in how they process others [19]. Therefore, this paper presents a novel approach for discovering exceptional subgroups of descriptions in which the content of those descriptions reliably differs from the general set of descriptions. We develop a novel interestingness measure for subgroup discovery appropriate for probability distributions across semantic representations. The proposed method is applied to a web-based experiment in which 500 raters describe images of 200 people. Our analysis identifies multiple exceptional subgroups and the attributes of the respective raters and images. We further discuss implications for intelligent systems.
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The difference between documents was calculated as the sum across all pairs of descriptions of the cosine similarity of the topic probability distributions. The number of topics per document was calculated as the sum across all descriptions of the conditional entropy of the topic probability distribution.
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Acknowledgments
Funding for data collection was provided by a University of Adelaide Interdisciplinary Research Grant to C. Semmler, A. Hendrickson, R Heyer, A. Dick and A. van den Hengel. Furthermore, this work has also been partially supported by the German Research Council (DFG) under grant AT 88/4-1.
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Hendrickson, A.T., Wang, J., Atzmueller, M. (2018). Identifying Exceptional Descriptions of People Using Topic Modeling and Subgroup Discovery. In: Ceci, M., Japkowicz, N., Liu, J., Papadopoulos, G., Raś, Z. (eds) Foundations of Intelligent Systems. ISMIS 2018. Lecture Notes in Computer Science(), vol 11177. Springer, Cham. https://doi.org/10.1007/978-3-030-01851-1_44
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