Abstract
The problem of detecting hidden latent regularities in large-scale applied data is investigated. A new approach is presented, where the possibilities of using multidimensional analogs of image processing and understanding methods adapted for higher dimensions are studied. Several promising options for combining practical competencies while solving applied problems from these positions are presented, and some prospects for further development of the approach are outlined.
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Acknowledgment
This work was supported in part by project 20-01-00609 of the Russian Foundation for Basic Research.
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Nelyubina, E., Ryazanov, V., Vinogradov, A. (2023). Analogs of Image Analysis Tools in the Search of Latent Regularities in Applied Data. In: Rousseau, JJ., Kapralos, B. (eds) Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges. ICPR 2022. Lecture Notes in Computer Science, vol 13644. Springer, Cham. https://doi.org/10.1007/978-3-031-37742-6_41
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