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  • Given N data vectors from n-dimensions, find kn 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)
  • Works for numeric data only

Principal Component Analysis (Steps)


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