How to compare the trucate SVD ,PCA, and T-SNE?
What we can say about features if t-SNE and PCA and truncate SVD digaram is in this figure?
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Sign up to join this communityTruncatedSVD is very similar to PCA, but differs in that it works on sample matrices directly instead of their covariance matrices. When the columnwise (per-feature) means of are subtracted from the feature values, truncated SVD on the resulting matrix is equivalent to PCA. In practical terms, this means that the TruncatedSVD transformer accepts scipy.sparse matrices without the need to densify them, as densifying may fill up memory even for medium-sized document collections.