| 31. | For a second analysis, the scores from both cultures were combined, and principal components analysis was used.
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| 32. | This is to be contrasted with principal component analysis which seeks to minimize the mean square error of all residuals.
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| 33. | Face images usually occupy a high-dimensional space and conventional principal component analysis was intractable on such data sets.
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| 34. | For each area, it learns a separate Principal Component Analysis ( PCA ) basis and reconstructs the area separately.
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| 35. | This problem is a shortcoming of principal component analysis in general, not just of M-SSA in particular.
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| 36. | Ordinary principal component analysis ( PCA ) uses a vector space transform to reduce multidimensional data sets to lower dimensions.
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| 37. | By applying principal component analysis to data from " classical genetic markers " ( protein cline with a Near Eastern focus.
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| 38. | Since centered data is required to perform an effective principal component analysis, we'centralize'K to become K'
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| 39. | These are then reduced to 10-15 dimensions by principal component analysis, giving the appearance information A \,.
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| 40. | Random Project does not perform as well as Principal Component Analysis at preserving inter-point distances but is computationally cheaper.
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