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Principal component analysis (PCA) is a linear dimensionality reduction technique. It reduces a multivariate dataset to a smaller set of constructed variables preserving as much information (as much variance) as possible. These variables, called principal components, are linear combinations of the input variables.

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Can I multiply samples' scores in PCA to project new data?

Normally, I performed PCA on matrix A and got a low-dimensional representation of m1 samples, and then projected m2 samples into the low-dimensional subspace via multiplying the rotation matrix. … Now I am thinking about what if I perform PCA on matrix A's transposition, and then I will get n variables (now they are samples)'s score matrix M. …
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