sparsity


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The Jacobian of function F has an arrowhead sparsity structure, as shown in Figure 2 for n = 50.
Integrating trust and similarity to ameliorate the data sparsity and cold start for recommender systems
For developing an efficient DNN classifier, some tuning parameters such as sparsity, the regularization term and the weight of the sparsity penalty term must be determined.
It is an extension of sparse PCA, where the sparsity patterns of all dictionary elements are structured and constrained to belong to a pre-specified set of shapes.
Table 1--Maximum Likelihood Values of Different Sparse Degrees (The Number of Nodes of the Brain Region Is 90) Sparsity 5% 10% 15% 20% Log- -200.
max] which controls the tradeoff between sparsity and reconstruction error.
2] are the tuning parameters for sparsity and low rankness, respectively.
The merit of SBL is its flexibility in modeling sparse signals that can not only promote the sparsity but also exploit the possible structure of the signal to be recovered [20].
The concept of spatial sparsity is useful in DOA estimation problem because it allows to hold only the information of interest and to set to zero unnecessary one.
In this paper, the sparsity of the beam pattern is used in the [D.
The minimum number of measurements required for reliable signal recovery depends on the sparsity of the signal representation with the sparsifying basis.
There has been some work in the literature to increase the scalability by reducing the dimensions of the recommender system dataset using singular value decomposition (SVD); however, due to sparsity it results in inaccurate recommendations.