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This is similar to the work performed by Satake and Shakunaga [27] who have used sparse template matching to compute the weight of the sample for.
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A sly tempo change between verse and chorus, meanwhile, suggested that production team the Bomb Squad – Eric Sadler, and brothers Hank and Keith Shocklee – had ambitions beyond early hip-hop's sparse, simple template.
In the synthetic case: (i) a data matrix X0 ∈ ℝ50×300 was created from the Gaussian distribution; (ii) a sparse intrinsic template T ∈ ℝ300×5 with 50 non-zero rows and a small set of randomly generated perturbation matrices P d ∈ ℝ5×5 were created for each d = 1, 2,…, D task; and (iii) the responses (e.g. target values) were then determined by Y d = X0 TP d + ϵ, where ϵ is the noise term.
Mei et al. [15] first propose the L1 tracker by casting the tracking problem as finding a sparse combination of a target template set and a trivial template set to approximate the target object.
The second term is the reconstruction error of sparse representation by target templates with CNN features.
Wang et al. [19] introduce l 1 regularization into the principal component analysis (PCA) reconstruction and propose an online tracking algorithm, which approximates the target by linearly combining the PCA basis and a sparse set of trivial templates.
Exemplar-based methods have the potential in addressing the deficiency of HMMs and in recent years they have drawn renewed attention in the speech recognition community [7, 8], such as sparse representations (SRs) [9] and template matching [10, 11].
Previous reports of a DNA polymerase utilizing a deoxynucleoside diphosphate as a substrate in a template dependent manner are sparse.
The local TF template matching builds an effective TF matching-based sparse representation approach with the merit of satisfying the native pulse waveform structure of transients.
Mei and Ling [5] first explored sparse representation into an on-line tracking system, where a trivial template with high dimension is introduced to approximate noise and occlusion.
Precisely, to represent the particle with relatively little error, sparse representation usually requires a relatively large dictionary (with a trivial template) and relatively dense coefficients, which both will increase the solution complexity.
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