Isometric Feature Mapping (ISOMAP) not only has high complexity but also can not learn new samples.
等距特征映射(ISOMAP)不仅计算复杂度很高，而且缺乏对新样本的学习能力。
The experiments show that the results from the new method are close to those from ISOMAP and are superior to those from L-ISOMAP.
实验结果表明，从该算法得到的结果与从ISOMAP得到的结果相近，且优于从L-ISOMAP得到的结果。
Then, combining IMD-Isomap and generalized regression neural network, which has a good ability for approximation, a classifier is proposed.
然后，结合泛化回归神经网络，设计出一种分类器。
This paper applies it to ISOMAP, and experimental results validate the optimization of the neighborhood and the accurate of the embedding results.
实验结果表明，将这种算法应用于ISOMAP后，邻域得到进一步优化，嵌入结果也更加准确。
The success of ISOMAP depends greatly on being able to choose a suitable neighborhood size, however, it is still an open problem how to do this effectively.
ISOMAP算法能否被成功运用，很大程度上依赖于邻域大小的选取是否合适。
This paper applies manifold learning method in aircraft image identification, and proposes an algorithm of aircraft identification based on improved Isometric Mapping(ISOMAP).
将流形学习方法应用于飞机图像识别中，提出一种基于改进等距映射（ISOMAP）的飞机识别算法。
This paper applies manifold learning method in aircraft image identification, and proposes an algorithm of aircraft identification based on improved Isometric Mapping(ISOMAP).
将流形学习方法应用于飞机图像识别中，提出一种基于改进等距映射（ISOMAP）的飞机识别算法。
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