说明： 稀疏矩阵是机器学习中经常遇到的一种矩阵形式，特别是当矩阵行列比较多的时候，本着“节约”原则...
To this end, we state the task of multiloudspeaker panning as an 1 optimization problem. We demonstrate and prove that the resulting solutions are exactly sparse. Moreover, we show the effect of adding a nonnegativity constraint on the loudspeaker gains in order to preserve the locality of the...
Of the approximately 2,700 M2 families screened, we identified four recessive sparse panicle mutants (spp1–spp4) characterized by reduced and uneven branching of the inflorescence. To identify the gene underlying the sparse panicle1 (spp1) phenotype, we performed bulked segregant analysis and deep...
The energy compaction feature of such sparse coefficient vectors is then evaluated in a lossy hyperspectral data compression framework. Experimental results on a number of hyperspectral data show that this approach is effective in hyperspectral data compression, and comparable to some of the state-of-...
This is a PR for the other parts in PR #2458 concerning sparse output support for the ovr classier. This branch was made off of the sprs-lbl-bin branch from PR #3203 and will be rebased to remove a...
商标名称 SPARSE 1 国际分类 第42类-网站服务 商标状态 商标注册申请 申请/注册号 55518521 申请日期 2021-04-23 申请人名称(中文) 墨芯人工智能科技(深圳)有限公司 申请人名称(英文) - 申请人地址(中文) 广东省深圳市南山区粤海街道高新区社区高新南九道55号微软科通大厦24D 申请人地址(英文) - 初审公告期...
We consider the problem of recovering polynomials that are sparse with respect to the basis of Legendre polynomials from a small number of random samples. In particular, we show that a Legendre s s mathContainer Loading Mathjax -sparse polynomial of maximal degree N N mathContainer Loading Math...
Function execution status. The enumeration type iskml_sparse_status_t. Dependencies C: "kspblas.h" Fortran: "kspblas.f03" Examples C interface: KML_INT nz = 2; KML_Complex8 dotci = {0, 0}; KML_INT indx[2] = {1, 2}; KML_Complex8 x[2] = {{1, 2}, {3, 4}}; KML_Comp...
For most large underdetermined systems of linear equations the minimal 1-norm solution is also the sparsest solution We consider linear equations y = Φx where y is a given vector in ℝn and Φ is a given n × m matrix with n < m ≤τn, and we wish to solve ......
In Section 3, we present a formulation of CCA using a rank-1 matrix approximation of the orthogonal projectors of data sets and derive the smoothed solution. In Section 4, we introduce our new sparse CCA algorithm. In Section 5, we present some simulation results to demonstrate the ...