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Data should be standardized for calculating principal components. If data is standardized before aggregating by country, the resultant data after aggregation will no longer will have mean 0 and standard deviation 1, hence, the results of PCA will be erroneous. Therefore, data should be aggregated and then standardized in order to obtain correct principal ...


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One way to explore the mapping between the original dimensions and and PCA dimensions is to look at something called the factor loadings. These are essentially projections of your original dimensions into your PCA space. From this, you can see which of your original features are aligned with your new dimensions, or are aligned with one another. An example of ...


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You could also check t-SNE which is a dimensionality reduction technique based on the probability distribution


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Two problems: Your sorting is incorrect: eVec = eVec1[np.flip(np.argsort(eVal1))] sorts the rows of the matrix, but you want to sort the columns. Replacing this with eVec = eVec1[:, np.flip(np.argsort(eVal1))] fixes this issue. The sign of the eigenvectors are sometimes opposite. (That's fine, being an eigenvector is scale invariant, and while both np....


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Your task is achieved by Subspace Clustering


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Welcome to the site. PCA is an unsupervised dimensionality reduction algorithm. It works by transforming the original feature-set into eigen-vectors that are difficult to map with the original feature set. As such, the first Principal Component (PC) contains the features with maximum variance. The subsequent PCs contain features with decreased variance to ...


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