Recently, manifold learning has been attracting many attentions in the field of machine learning.
流形学习方法已经成为当前机器学习领域研究的一个热点。
A noise reduction method in nonlinear time series based on phase reconstruction and manifold learning was proposed.
提出了一种基于相重构和主流形识别的非线性时间序列降噪方法。
At present, manifold learning has become a hot issue in the field of pattern recognition, machine learning and data mining.
目前，流形学习已成为模式识别、机器学习和数据挖掘领域的研究热点问题。
Meanwhile, the paper first attempted to apply the improved manifold learning algorithm to the hospital performance evaluation.
同时，首次尝试将该改进的流形学习算法应用于医院绩效考核。
Locally linear embedding (LLE) is a widely-used manifold learning algorithm, in this paper we improve on the algorithm and put it to use in spatial data index.
局部线性嵌入方法是一种应用广泛的流形学习方法，本文提出算法的一种改进，并将其应用于空间数据索引。
Lie group Machine learning (LML) inherit the advantages of manifold learning method and make full use of the Lie group's structure of algebraic and geometry in mathematics.
李群机器学习(LML)既继承了流形学习的优点，又充分利用了李群的代数结构和几何结构的数学本质，自提出以来就引起了许多研究者的关注。
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。
Aiming at the sensitivity of manifold learning, such as locality preserving projection (LPP), to noises or outliers, a robust version of LPP, called robust LPP (RLPP), was proposed.
针对局部保持投影的流形学习算法对于噪声与异常值的敏感性，提出了一种鲁棒的局部保持投影算法。
Different from the existent noise reduction methods in nonlinear time series, the method based on principal manifold learning emphasized more on the global structure of time series.
与现有的非线性时间序列消噪算法不同，基于主流形的消噪算法更强调时间序列的整体结构。
Manifold learning is helpful to the discovery of the intrinsic distribution and geometry structure of data.
流形学习有助于发现数据的内在分布和几何结构。
Currently, Manifold learning gradually becomes a hotspot in the field of machine learning and pattern recognition.
月前，流形学习渐渐成为机器学习及模式识别领域中的一个研究热点。
Manifold learning attempts to obtain the intrinsic structure of non-linearly distributed data, which can be used in non-linear dimensionality reduction(NLDR).
流形学习旨在获得非线性分布数据的内在结构，可以用于非线性降维。
As an effective manifold-learning method, the local tangent space alignment (LTSA) algorithm is sensitive to outliers.
局部切空间排列(LTSA)算法是一种有效的流形学习方法，但该算法对孤立点的存在非常敏感。
As an effective manifold-learning method, the local tangent space alignment (LTSA) algorithm is sensitive to outliers.
局部切空间排列(LTSA)算法是一种有效的流形学习方法，但该算法对孤立点的存在非常敏感。
应用推荐