A robot arm carries three strain sensors , each reading in centimetres. Over a long logging run the readings are mean-centred, and their covariance matrix (in ) comes out as
All three eigenvalues of are integers.
PCA takes the first principal direction to be the unit eigenvector belonging to the largest eigenvalue of . Such a unit eigenvector is only determined up to an overall sign, so this pipeline fixes the sign with the usual convention:
Sign convention. Orient so that its first component is positive.
A fresh reading arrives, already mean-centred:
| Sensor | Deviation from mean (cm) |
|---|---|
Its PC1 score is its coordinate along that first principal direction,
What is ?
Round your answer to two decimal places.
Once you get it right, the reasoning behind the answer shows up here.
Toggle to project every point onto the principal component.
Instead of storing two coordinates, we now store one position along the line. The important pattern remains.