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- W3115898288 abstract "In this paper, we evaluate dimensionality reduction methods in terms of difficulty in estimating visual information on original images from dimensionally reduced ones. Recently, dimensionality reduction has been receiving attention as the process of not only reducing the number of random variables, but also protecting visual information for privacy-preserving machine learning. For such a reason, difficulty in estimating visual information is discussed. In particular, the random sampling method that was proposed for privacy-preserving machine learning, is compared with typical dimensionality reduction methods. In an image classification experiment, the random sampling method is demonstrated not only to have high difficulty, but also to be comparable to other dimensionality reduction methods, while maintaining the property of spatial information invariant." @default.
- W3115898288 created "2021-01-05" @default.
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- W3115898288 date "2020-10-13" @default.
- W3115898288 modified "2023-09-23" @default.
- W3115898288 title "Difficulty in estimating visual information from randomly sampled images" @default.
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- W3115898288 doi "https://doi.org/10.1109/gcce50665.2020.9291845" @default.
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