| 1. | Coherent interference suppression with eigenspace - based beamformer
基于特征空间的相干干扰抑制技术 |
| 2. | A modified eigenspace - based algorithm for adaptive beamforming
一种改进的基于特征空间自适应波束形成算法 |
| 3. | Additive relative perturbation bounds for eigenspace and singular subspace
矩阵特征空间和奇异空间相对扰动界 |
| 4. | Research on an ameliorated spatial smoothing technology based on eigenspace
一种改进的特征子空间平滑技术研究 |
| 5. | Then , eigenspace transformation based on pca is applied to time - varying project
然后,应用主成分分析( pca )进行特征提取和压缩。 |
| 6. | We emphasized the selection of the eigenvector which used to create the eigenspace
针对其在组成特征投影空间时特征向量选择问题的做了重点研究。 |
| 7. | In the fourth chapter we deal with the eigenspace ( see 4 . 1 ) and singular space ( see 4 . 2 ) perturbations
第四章讨论了特征空间(见4 1 )和奇异空间(见4 2 ]的扰动。 |
| 8. | This is a two - objective optimization problem . to solve the problem , two methods are presented , which are the weighted method and the eigenspace method
文中还给出了两种确定权值的方法，即加权法和特征空间法，并通过计算机仿真对这两种方法进行了比较。 |
| 9. | In term of the relative gaps of the eigenvalues and the singular values , the additive relative perturbation bounds of eigenspace and singular subspace of matrices are investigated and some new results are obtained
摘要根据特徵值和奇异值的相对分离情况，研究了矩阵特徵空间和奇异空间的加法相对扰动界，得到一些新的扰动界。 |
| 10. | Firstly this paper introduced the principle of fisher linear discriminant function , and then present the subspace lda algorithm which project the data that is in high dimension space to eigenspace that is in low space and then maximize discriminant coefficient
本文介绍了fisher线性判别准则原理和实现过程，然后引入subspacelda方法，用pca将高维图像数据投影到低维的特征脸空间，再用lda最大化判别系数。 |
