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When looking at the eigenvector weights from the Principal Component Analysis (PCA). What is a viable...

When looking at the eigenvector weights from the Principal Component Analysis (PCA). What is a viable meaning for each of the first four PC axes?

Math   Statistics-And-Probability   
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Answer #1

Principle component analysis may be used to overcome the problem of over fitting. it is basically variable reduction technique. PCA converts the correlations among variables in data so that the can be visualized.

Axes of PCA are ranked in order of importance. Differences among the first principle component axis is more important than that of second principle component axis and so on.

Total number of PC axes obtained are equal to number of variables. Together they explain 100% variation in the data (in a decreasing order).

first 4 PC axes are obtained using first 4 maximum eigen values and corresponding eigen vectors. It tries to view the data in 4 dimensional space. hence 4 axes.

If we choose 4 PCA for our data it means that they are all important to study maximum variations in the data.

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