It is also frequently used for data compression, exploration, and visualization. DR can be viewed as a method for latent feature extraction. Original high-dimensional data often contain measurements on uninformative or redundant variables. ![]() Low-dimensional data representations that remove noise but retain the signal of interest can be instrumental in understanding hidden structures and patterns. By reducing the dimensionality of the data, you can often alleviate this challenging and troublesome phenomenon. ![]() Even if the number of collected data points is large, they remain sparsely submerged in a voluminous high-dimensional space that is practically impossible to explore exhaustively (see chapter 12 ). Because of “the curse of dimensionality,” many statistical methods lack power when applied to high-dimensional data. Both a means of denoising and simplification, it can be beneficial for the majority of modern biological datasets, in which it’s not uncommon to have hundreds or even millions of simultaneous measurements collected for a single sample. Dimensionality reduction (DR) is frequently applied during the analysis of high-dimensional data.
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