partial tucker decomposition-CodePudding

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Usually you have better result with a balanced rank, i.e. i and j not very different from each other.

As we increase the error we can get better compression, we can rank the (i,j) by the error, and plot only where the error is minimum for a given feature dimension i * j, like this

X = np.zeros([28, 28]) X[...] = np.nan; p = 28 * 28; for e,i,j in sorted([(err[i,j], i, j) for i in range(1, 28) for j in range(1, 28)]): if p < i * j: # we can achieve this error with some better compression pass else: p = i * j; X[i,j] = e; plt.imshow(X)

Anywhere in the white region you are wasting resources, the choice