Issue 1453: MDS: fall back to SVD when the full similarity matrix is known #3141
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| K = -0.5*np.dot(H, np.dot(similarities**2, H)) | ||
| w, V = np.linalg.eig(K) | ||
| # Sort eigenvalues and eigenvectors in decreasing order | ||
| ix = np.argsort(w)[::-1] |
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w is already sorted by default, no?
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Addressed in my branch (else, no, but I've switched to sparse.linalg.eigs, which does).
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Here is a new PR : #4485 |
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closing as replaced by #4485. |
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The SVD method can now be used to calculate the metric MDS. In order to do this a new argument
methodwhich defaults tosmacofand can be set tosvdwas introduced in the MDS estimator.This was done in order to fix issue #1453.