[翻譯] Understanding Principal Components Analysis (PCA)

[翻譯] Understanding Principal Components Analysis (PCA)

2020/10/12

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https://www.neuraldesigner.com/blog/principal-components-analysis

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Principal components analysis (PCA) is a statistical technique that allows identifying underlying linear patterns in a data set so it can be expressed in terms of other data set of a significatively lower dimension without much loss of information.

主成分分析（PCA）是一種統計技術，可以識別數據集中的基本線性模式，因此可以根據其他維度較低的數據集來表達它，而不會丟失太多信息。

The final data set should explain most of the variance of the original data set by reducing the number of variables. The final variables will be named as principal components.

最終數據集應通過減少變量數量來解釋原始數據集的大部分差異。 最終變量將被命名為主成分。

To illustrate the whole process, we will make use of the following data set, with only 2 dimensions.

為了說明整個過程，我們將使用以下僅有 2 個維度的數據集。

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References

Understanding principal components analysis (PCA) | Neural Designer

https://www.neuraldesigner.com/blog/principal-components-analysis