Abstract
In this paper, we propose a method for simultaneous analysis of subjective and objective data. The method, named coupled tensor self-organizing map (SOM), consists of two tensor SOMs, one of which learns the subjective data while the other learns the objective data. The coupled tensor SOM visualizes the dataset as three maps, namely, one target object map, and two survey item maps corresponding to the subjective and objective data. This method can be further extended to generate extra maps such as a map of attributes. In addition, the coupled tensor SOM also provides an interactive visualization of the relationship between the target objects and the survey items by coloring these three maps. We applied our proposed method to the wine aroma dataset. Our results indicate that this method facilitates an intuitive overview of the dataset.
This work was supported by JSPS KAKENHI Grant Number 18K11472 and ZOZO Research.
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Notes
- 1.
To be precise, they must be treated as discrete variables, but in this paper we treat them as Gaussian random variables to simplify the explanation.
- 2.
Wine-21 is a French red wine produced in Collioure, made from Grenache Gris and Carignan. This wine has aromas of “black cherry compote”, “blackberry compote”, “violet”, and “spicy”.
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Yoneda, K., Nakano, K., Horio, K., Furukawa, T. (2018). Simultaneous Analysis of Subjective and Objective Data Using Coupled Tensor Self-organizing Maps: Wine Aroma Analysis with Sensory and Chemical Data. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11306. Springer, Cham. https://doi.org/10.1007/978-3-030-04224-0_3
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