In recent years, one-bit compressed sensing (CS) has been applied to the field of synthetic aperture radar (SAR) imaging and shows great potential. The existing models are, based on application of the sensing...
Gradshteyn, I. S. and Ryzhik, I. M.Tables of Integrals, Series, and Products, 6th ed.San Diego, CA: Academic Press, pp. 1114-1125, 2000. Horn, R. A. and Johnson, C. R. "Norms for Vectors and Matrices." Ch. 5 inMatrix Analysis.Cambridge, England: Cambridge University Press, 199...
In this paper, we present two nuclear-L-1 norm joint matrix regression (NL1R) models for face recognition with mixed noise, which are derived by using MAP (maximum a posteriori probability estimation). The first model considers the mixed noise as a whole, while the second model assumes the...
c = condest(A) computes a lower bound c for the 1-norm condition number of a square matrix A. c = condest(A,t) changes t, a positive integer parameter equal to the number of columns in an underlying iteration matrix. Increasing the number of columns usually gives a better condition est...
Return the Norm of the vector over axis 1 in Linear Algebra in Python - To return the Norm of the matrix or vector in Linear Algebra, use the LA.norm() method in Python Numpy. The 1st parameter, x is an input array. If axis is None, x must be 1-D or 2-D,
We also find two upper bounds on the sparseness of 1-norm SVND, or exact support vector (ESV) and kernel Gram matrix rank bounds. The ESV bound indicates that 1-norm SVND has a sparser representation model than SVND. The kernel Gram matrix rank bound can loosely estimate the sparseness...
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Meanwhile, the response variables of RFS is the binary class label {0,1}, not the test samples as shown in our novel model. Finally, the coefficient matrix obtained in RFS method is used for detecting non-zero elements and then selecting out the associated features, however, it is ...
Whereas present L2, 1-PCA algorithms implement dimension reduction on the rank of the matrix and the rank is complex problem. In order to solve this problem, this paper proposed using trace norm instead of rank, then the calculation of L2, 1-PCA algorithm could simplify and the efficiency ...
So I'm already convinced that ∥A∥‖A‖ is (when considering AA as a matrix operator) the column of AA with maximum l1l1-norm. But I'm hitting a wall in my proof. Here's what I've got so far. Let A^=max1≤i≤n∑mj=1|Aji|A^=max1≤i≤n∑j=1m|Aji|,...