Table 1 QCML estimator running times for the data sets analyzed.
From: Robust estimation of the intrinsic dimension of data sets with quantum cognition machine learning
Dataset | D= no. features | T= no.samples | N= Hilbert space dimension | Running time |
|---|---|---|---|---|
Sphere | 3 | 2500 | 3 | 2.9s |
\(M_{10b}\) | 18 | 2500 | 16 | 3.5s |
\(M_{\beta }\) | 40 | 2500 | 16 | 5.4s |
\(MN_{1}\) | 72 | 2500 | 16 | 7.2s |
ISOMAP faces | 4096 | 698 | 32 | 142s |
MNIST - digit 1 | 784 | 1135 | 32 | 28.2s |
Breast Cancer | 30 | 569 | 16 | 1.9s |
