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Adopting Mental Similarity Notions of Categorical Data Objects to Algorithmic Similarity Functions
Abstract
Similarity functions are essential for many analytical tasks.
Goal: create a similarity function based on visual-interactive user
feedback to capture the Mental Similarity Notion in the heads of domain experts.
Inspiring solutions for numerical data exist.
However, the interpretation of user feedback for categorical data attributes poses
additional challenges (see Test Case).
Our Solution: a Feedback Model with an additional improvement step for categorical attributes.
... Similarly, the evaluation of VIAL approaches is still in an early state. We observe that several existing works already validate learning models, as well as the model conversion process [15][16][17][18]67]. However, many other types of evaluation are possible. ...
The assignment of labels to data instances is a fundamental prerequisite for many machine learning tasks. Moreover, labeling is a frequently applied process in visual interactive analysis approaches and visual analytics. However, the strategies for creating labels usually differ between these two fields. This raises the question whether synergies between the different approaches can be attained. In this paper, we study the process of labeling data instances with the user in the loop, from both the machine learning and visual interactive perspective. Based on a review of differences and commonalities, we propose the “visual interactive labeling” (VIAL) process that unifies both approaches. We describe the six major steps of the process and discuss their specific challenges. Additionally, we present two heterogeneous usage scenarios from the novel VIAL perspective, one on metric distance learning and one on object detection in videos. Finally, we discuss general challenges to VIAL and point out necessary work for the realization of future VIAL approaches.
... We neglect strategies for arranging small sets of more than two instances in 2D since we explicitly want to include categorical and boolean attributes. It has been shown that the interpretation of relative distances for categorical data is non-trivial ( Sessler et al., 2014). ...
... We neglect strategies for arranging small sets of more than two instances in 2D since we explicitly want to include categorical and boolean attributes. It has been shown that the interpretation of relative distances for categorical data is non-trivial ( Sessler et al., 2014). ...
The definition of similarity is a key prerequisite when analyzing complex data types in data mining, information retrieval, or machine learning. However, the meaningful definition is often hampered by the complexity of data objects and particularly by different notions of subjective similarity latent in targeted user groups. Taking the example of soccer players, we present a visual-interactive system that learns users' mental models of similarity. In a visual-interactive interface, users are able to label pairs of soccer players with respect to their subjective notion of similarity. Our proposed similarity model automatically learns the respective concept of similarity using an active learning strategy. A visual-interactive retrieval technique is provided to validate the model and to execute downstream retrieval tasks for soccer player analysis. The applicability of the approach is demonstrated in different evaluation strategies, including usage scenarions and cross-validation tests.
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