## 5 Conclusions  
In this paper, a self-learning subspace matrix factorization (SLSMF) method is introduced to speed up the faithful reconstruction of highly-accelerated NMR spectroscopy. Comparisons with the state-of-the-art self-learning subspace method and the low-rank method imply that the new approach remarkably reduces computation time without sacrificing spectrum quality.  
The following are available online at [[http://www.mdpi.com/2076-3417/10/11/3939/s1](http://www.mdpi.com/2076-3417/10/11/3939/s1)]([http://www.mdpi.com/2076-3417/10/11/3939/s1](http://www.mdpi.com/2076-3417/10/11/3939/s1)), Figure S1: Low-fidelity reconstruction results for traditional methods, Figure S2: Moderate-fidelity reconstruction results for traditional methods, Figure S3: High-fidelity reconstruction results for traditional methods, Table S1: Statistic of peaks correlations in Figure S1, Table S2: Statistic of peaks correlations in Figure S2, Table S3: Statistic of peaks correlations in Figure S3.  
**Author Contributions:** Experiments and simulations, Z.T. and J.Z.; methodology, D.G.; writing--original draft preparation, Z.T., H.L. and D.G.; project administration, D.G.; conceptualization, supervision, funding acquisition,  
Figure 12: The computation time of spectra reconstruction on a personal computer. (**a,b**) computation time of spectra reconstruction without and with parallel computing, respectively. Note: All reconstruction algorithms were performed using MATLAB 2017b (Mathworks Inc., Natick, MA, USA) on a personal computer with 1.80 GHz 4 cores CPU and 8 GB RAM. The switching on and off parallel pool takes an average of 24 s.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Method Quality**} & \multirow{2}{*}{\begin{tabular}{c} **Peak Intensity Correlation** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **High-Fidelity** \\ **Reconstruction** \\ \end{tabular} } & \multicolumn{2}{c}{
\begin{tabular}{c} **Reconstruction Time** \\ **(Seconds)** \\ \end{tabular} } & \multirow{2}{*}{**Fast**} \\ \cline{2-2} \cline{5-6}  & **Low Intensity** & & & & **Low Intensity** & **Peak** \\  & **Peaks** & & & **Computing** & **Personal** & **Reconstruction** \\ \hline LRHM & 0.9209 & 0.9826 & No & 116.5 & 138.2 & No \\ LRHMF & 0.9222 & 0.9870 & No & 69.0 & 82.0 & Yes \\ SLS & 0.9865 & 0.9979 & Yes & 290.2 & 381.8 & No \\ SLSMF & 0.9877 & 0.9979 & Yes & 154.5 & 211.5 & Neutral \\ \hline \end{tabular} Note: R\({}^{2}\) denotes the square of the Pearson correlation coefficient. The correlation of low-intensity peaks and all peaks are averaged on all spectra in Figures 7–9. The reconstruction time are averaged on all spectra in Figures 11 and 12. The Pearson correlation coefficients of low-intensity peaks and all peaks are greater than 0.95 and 0.99, respectively, indicating high-fidelity reconstruction.  
\end{table}
Table 5: Performance comparison of various methods.  
D.G.; manuscript preparation, all authors. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the National Natural Science Foundation of China (61871341 and 61672335), and the Science and Technology Program of Xiamen (3502Z20183053).  
**Acknowledgments:** The authors are grateful to [PERSON] and [PERSON] for providing the 2D HSQC spectrum of GB1 and [PERSON] for providing the solid NMR. Thanks to [PERSON] and [PERSON] for valuable suggestions on this manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.