Many modern data analysis methods involve computing a matrix singular value decomposition(SVD)or eigenvalue decomposition(EVO).Principal component analysis is the time-honored example,but more recent applications include latent semantic indexing(LSI),hypertext induced topic selection(HITS),clustering,classification,etc.Though the SVD and EVD are well established and can be computed via state-of-the-art algorithms,it is not commonly mentioned that there is an intrinsic sign indeterminacy that can significantly impact the conclusions and interpretations drawn from their results.Here we provide a solution to the sign ambiguity problem and show how it leads to more sensible solutions.
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