Introduction to Introduction to Sensory Data Analysis - Camo

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4. Principal Component Analysis: PCA / a) Theory Principal Component Analysis (PCA) •Exploratory data analysis •Extract information • Noise removal • Dimensionality reduction Data structure in PCA: • Each row represents an observation • Each column represents a variable Variable 1 Variable 2 Variable 3 Object 1 Object 2 Object 3 Object 4 X Model Error Data Structure Noise
4. Principal Component Analysis: PCA / a) Theory Principal Component Analysis (PCA) New latent variables that are linear combinations of the original variables. PC1 = a 1 V1 + a 2 V2 + a 3 V3 X = Mean + b 1 PC1 + b 2 PC2 + Error Constraints : • Maximise the dispersion of samples along the latent variables (the variance) • Orthogonality PCA = A change of variable space
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Page 5 and 6: 1. Why sensory data analysis Can we
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