介绍了一维噪声子空间算法的基本思想、原理以及算法步骤。
This paper analyses the thought and approach of Noise-Subspace of One dimension algorithm.
针对高维数据的相似性度量问题，提出了一种基于子空间的相似性度量方法。
Aiming at the similarity measurement of high dimensional data, the paper put forward a new method based on subspace.
分析了MUSIC算法和一维噪声子空间算法的性能，研究了多径衰落对这两种算法的影响。
We not only analyse performance of MUSIC algorithm and 1-d noise subspace algorithm but also investigate the effect of multipath on these two algorithms.
进而将两种SVD算法应用在特征子空间雷达目标一维距离像识别法中，使用实测数据对其进行速度验证和性能评估。
Then, the performance of two SVD algorithms and feature subspace radar target recognition algorithm based on SVD are evaluated according to real data of planes.
讨论了有限维和无限维复J-辛空间上的拓扑，并证明了复J-辛空间的每一个完全J-Lagrangian子流形都是闭集。
We discuss topologies for complex J-symplectic spaces and prove that each complete J-Lagrangian submanifold of the complex J-symplectic spaces a closed set.
讨论了有限维和无限维复J-辛空间上的拓扑，并证明了复J-辛空间的每一个完全J-Lagrangian子流形都是闭集。
We discuss topologies for complex J-symplectic spaces and prove that each complete J-Lagrangian submanifold of the complex J-symplectic spaces a closed set.
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