方法证据记录
Principal Component Analysis
Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures.
源记录
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Principal Component Analysis (PCA)
分类方法记录 · ml-model / machine-learning
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