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- W4320801948 abstract "Data is distributed randomly in high dimensions which are highly correlated. When processed directly, these high dimensional data can cost high computational costs and lead to overfitting. This high-dimensional data contains redundant and irrelevant information that might affect the model's accuracy. Hyperspectral imaging uses the concept of spectrology which analyzes a wide range of spectral bands instead of assigning RGB Colors. Dimensional reduction of high dimensional data is the method of reducing the number of input variables while taking into account relevant information according to a set of principles. This paper gives a review of various deep learning algorithms used for dimensional reduction." @default.
- W4320801948 created "2023-02-15" @default.
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- W4320801948 date "2022-11-09" @default.
- W4320801948 modified "2023-09-30" @default.
- W4320801948 title "A Comprehensive Analysis of techniques used for Dimensional Reduction of HSI Images" @default.
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- W4320801948 doi "https://doi.org/10.1109/iccst55948.2022.10040360" @default.
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