Principal Component Analysis (PCA)
Orthogonal Variance Maximization via Covariance Eigendecomposition & SVD
#Linear Algebra#Dimensionality Reduction#Eigendecomposition#SVD#Unsupervised
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STAGE 1 / 7— Anti-Pattern
Section 1: The LLM Anti-Pattern vs Right-Sized Model
The Naive Generative LLM Approach:
Pasting 500 numerical tabular features into an LLM prompt to ask: "Summarize the 3 most important dimensions of variation in this customer data".
Why It Fails in Production:
LLMs cannot compute covariance matrices or matrix singular value decompositions (SVD). PCA extracts the exact mathematical orthogonal axes of maximum variance in 5ms.
Targeted Algorithm (Principal Component Analysis (PCA))
Latency:4ms (LAPACK SVD)
Cost / 1M Ops:$0.00
Determinism:Analytically Exact Vectors
Generative LLM Alternative
Latency:3,500ms
Cost / 1M Ops:$14,000
Determinism:Hallucinated Numerical Projections