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This function is more flexible than the classic soft and hard thresholds.
In order to recognize maneuvers involving a waiting period at a stop line for example, one has to resort to hard thresholds to identify waiting intervals and ignore them when computing the distance between two trajectories.
The comparison includes the optimum hard MSE, which represents the minimum MSE when the noisy coefficients are used as hard thresholds, along with the resulting MSE of different approaches.
In Figure 3, soft and hard thresholds were presented by Donoho [9, 10], MATS Method was proposed by Khare et al. [14], Hyperbolic shrinkage was used by Wang [15].
In the fly literature, these segments of differing speed are typically referred to as activity and inactivity and are defined by setting hard thresholds on the movement.
Such soft thresholds probably characterize those found in most living systems, including in neurons and in human psychophysical thresholds [23], as mathematically hard thresholds (i.e., a Heaviside function) are idealizations.
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Soft thresholding provides smoother results in comparison with the hard thresholding whereas thresholding technique provides better edge preservation in comparison with the soft thresholding technique.
Soft thresholding and hard thresholding have some limitations in denoising of signal[29, 30].
Ollila et al. [50] derived an iterative hard thresholding algorithm coined Huber iterative hard thresholding (HIHT) to solve the problem (29).
Table 2 compares the MSE of the proposed hard thresholding method with that of two hard thresholding methods, VisuShrink and MDL.
Table 2 MSE comparison of different thresholding methods with the MNDL hard thresholding approach.
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