1) Sparse Component Analysis
稀疏成分分析
2) sparse component analysis(SCA)
稀疏成分分析(SCA)
3) Spca-Ten
稀疏主成分分析
4) Sparse component analysis
稀疏成份分析
1.
Based on the measurement model of inverse synthetic aperture radar within a small aspect sector,two imaging methods,named as FFT-based united algorithm and two-dimensional decoupled algorithm,are presented with the application of sparse component analysis.
根据小角度条件下的逆合成孔径雷达观测模型,利用稀疏成份分析方法给出了基于FFT的二维联合超分辨算法和二维解耦超分辨成像算法。
2.
Based on the technique of sparse component analysis, a conditionally-linear parameterized model is constructed for signal representation and an algorithm named sequential linear programming is presented to solve it.
利用稀疏成份分析技术,建立了信号表示的条件线性参数模型,提出了模型求解的序列线性规划算法。
3.
On this basis, a generalized version of regularized FOCUSS algorithm applicable to sparse component analysis is derived, and its conver.
由此导出了可用于稀疏成份分析的广义正则化FOCUSS算法,并证明了该算法的收敛性。
5) Sparse Principal Component
稀疏主成分
1.
The Application of Sparse Principal Component Analysis in Comprehensive Assessment
稀疏主成分在综合评价中的应用
6) sparse component analysis
稀疏分量分析
1.
Most of sparse component analysis algorithms suppose that source signals are sufficiently sparse or by wavelet packets or FFT.
已有的大多数稀疏分量分析算法都是假定源信号是充分稀疏的,或经过小波变换、Fourier变换等后是充分稀疏的,该论文对源信号的稀疏性要求放宽了条件,提出了一种新的基于超平面聚类的欠定混叠稀疏分量分析算法。
补充资料：稀疏
1.亦作"稀疎"。 2.不稠密。 3.犹言冷落﹐疏远。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条