Based on the low-rank matrix recovery theory, we apply low-rank regularization in multi-spectral dictionary learning procedure such that MLSDL can well solve the problem of multi-spectral face recognition with high levels of noise.
Specifically, inspired by the low-rank matrix recovery theory, we provide a multi-view dictionary low-rank regularization term to solve the noise problem.
The inter-channel correlation is explored by measuring the channel difference signals in the gradient domain, while the structural information is explored by nonlocal low-rank regularization.
Naturally, the performance is significantly better for the short term compared to the long term; however, we observe that both the MC and the SS-MC approaches achieve a very stable performance in both cases, suggesting that the low-rank regularization can provide strong benefits in this challenging scenario.
Geophysicists still prioritize low-frequency regularizations in waveform, impedance, elastic and AVO inversion (Zong et al. 2012; Kroode et al. 2013; Li et al. 2016a) to improve the stability of the inversion process and the rate of convergence to a precise earth model.
When the seismic data are seriously disturbed by random noise (low S/N), the regularization parameter (varepsilon^{2}) should be increased appropriately, otherwise conversely.
The LOWESS normalization was used as part of a pipeline in MIDAS, which includes total intensity normalization, LOWESS normalization, standard deviation regularization and low intensity filtering [48].
Background-subtracted raw data were normalized using the MIDAS pipeline (TM4, TIGR Genomics, Rockville, MD) according to Sioson et al. (2006) with the following steps: total intensity normalization, LocFit (LOWESS), standard deviation regularization and low intensity trim [ 25].
Although the effect of regularization is small at low SH degree, it is already present.
