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  • pca - Principal components analysis need standardization or . . .
    After some google, I get confused pca need the scalar be same So which should I use Which technique needs to do before PCA? Does pca need standardization? standardized values will always be zero, and the standard deviation will always be one Does pca need normalization? range zero to one or both ?
  • Why does PCA assume Gaussian Distribution?
    PCA does assume normal distribution of features See p 55 SAS book 1 or Rummel, 1970 2 or Mardia, 1979 3 If you expect the PCs to be independent, then PCA might fail to live to your expectations Assuming that the dataset is Gaussian distributed would guarantee that the PCs are independent Linearity Large variances have important structure The principal components are orthogonal
  • Meaning of curved line shape distribution in t-SNE plot
    From my own experience, if you have curved lines in your t-SNE plots, it usually implies your original data are scattered similarly in lines as well One thing you can confirm whether this hypothesis is correct is that you can do principle component analysis (PCA) on your original data and then plot the first two dimensions Usually, this plot will also be in curved lines Your explanation is
  • distribution - How can PCA be distributed among workers? - Data Science . . .
    Eigenvektor and -values Feature vector matrix which are the eigenvektor as as columns My question is where can I distribute the work I assume it is only the during the covariance matrix Do have some book recommendation that cover distributed PCA
  • clustering - What best correct algorithm procedure to cluster a dataset . . .
    I heard about PCA to reduce number of variables, and I was also curious if: 1-PCA is applicable to this cenario (dataset with a lot of sparse 0's) 2-PCA could solve my problem (because I think that even when a reduce to 2 3 variables, some rows could still be 0 for that particular column (symptom)
  • What is the difference between SMOTE before PCA and after PCA
    PCA try to keep the main characteristics of initial dataset in the compacted dataset; however, some useful information is lost during the PCA reduction SMOTE resampling is used to work on the sample domain and increase the variety of sample domain and balance the distribution of classes in the dataset
  • Why does the PCA Scores plot (PC1 vs PC2) flips when using extracted . . .
    You can generate various PCA Scores plots from this data, of course One option is to extract variables from the Gaussian (such as the max height of the distribution or its full width at half maximum and so on) and use this as input for creating your PCA scores plot
  • PCA - Error minimization and Variance Maximization
    I'm studying the PCA algorithm and the theory behind it I think I understood how does it work and the idea of dimension reduction of the data in order to find a new feature (component) that maximi


















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