Principal Component Analysis
نویسنده
چکیده
where each column is a data sample. Analyzing—and hopefully understanding— the result of an experiment often consists in uncovering regularity or structure in the data matrix. Unfortunately, measured data is often “messy” in the sense that it is too high-dimensional for us to detect structure by direct inspection and in the sense that noise and/or redundancy often impairs data visualization. Principal Component Analysis (PCA) is a handy tool to reveal structure via dimensionality reduction and denoising of the data. In a nutshell and loosely speaking, PCA consists in detecting characteristics “features” of the data that can be ranked by degree of relevance: the more relevant a feature, the more it explains the variability of the data. PCA is successful when considering only a few of the most relevant “features” is enough to describe the data satisfactorily. Thus, successful
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Principal component analysis or factor analysis different wording or methodological fault?
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