What to do with Principal Component Analysis when there is no dependent variable?
Principal Component Analysis (PCA) is an unsupervised statistical technique aimed at identifying the main patterns or directions of variation in data. Therefore, PCA does not require dependent variables. Its main goal is to reduce the dimensionality of the data while retaining most of its variability.
The primary purpose of PCA is to transform multi-dimensional data into a new coordinate system, where these new coordinates (called principal components) can capture the maximum variance in the data. The first principal component captures the largest variance in the data, the second principal component (orthogonal to the first) captures the next largest variance, and so on.
Therefore, when you perform PCA, you are only operating on input features or variables, without considering any dependent or response variables.
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