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.
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