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Given N data vectors from n-dimensions, find k ≤ n orthogonal vectors (principal components) that can be best used to represent data
Normalize input data: Each attribute falls within the same range
Compute k orthonormal (unit) vectors, i.e., principal components
Each input data (vector) is a linear combination of the k principal component vectors
The principal components are sorted in order of decreasing “significance” or strength
Since the components are sorted, the size of the data can be reduced by eliminating the weak components, i.e., those with low variance (i.e., using the strongest principal components, it is possible to reconstruct a good approximation of the original data)