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Tagged “pca”

2 articles.

6 min readUnsupervised Learning

The Direction That Changes When You Change Units

Twelve people, two measurements, and three different first principal components: in millimetres the answer is almost pure height, in metres almost pure weight, and in centimetres an even blend - with the correlation fixed at 0.9500 throughout. What that says about what PCA maximises, why a proportion of variance explained of 99.999% can be a statement about metres rather than about people, and what standardising actually chooses.

Machine LearningStatistics
8 min readUnsupervised Learning

Unsupervised Learning: Structure Without Labels

What changes when there is no response to predict: principal components as the direction of maximum variance, K-means and the local optima it settles into, hierarchical clustering and the linkage that decides the answer - and why none of the required choices can be validated the way a classifier can.

Machine LearningStatistics