From previous researches, we know that Principal Component Analysis (PCA) can't ensure better output. As PCA only cares about dimensionality reduction, hence, it may result in clustering the classes in a better way or damaging the clusters even more. Is there any mathematical or statistical way to calculate how PCA will affect the change in performance (better/worse/no change) if applied to a dataset?
Using PCA is important data pre-processing step for removing impurities/errors/noise/unwanted-details etc. It will improve accuracy, clarity, computation cost, good visualization, good recognition.
Typically PCA is used as a dimension reduction tool in post-processing stages in pattern recognition and computer vision applications. It helps in visualization of high-dimensional data and also reduces processing overheads.
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