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。
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.
针对这个缺点，提出了一种改进的、基于自适应最近邻法的局部线性嵌入方法，数值实验证明算法对于有监督的学习问题，具有较好的适应性。
An adaptive nearest neighbor locally linear embedding algorithm is proposed to overcome this shortage. Experiment results show that the algorithm ADAPTS well the supervised learning problems.
针对该问题，提出基于核局部线性嵌入算法的图像去噪方法。
Aiming at this problem, this paper USES Kernel Locally Linear Embedding (KLLE) algorithm to solve image denoising problem in this paper.
其主要思想是通过引入线性变换矩阵来近似经典的局部线性嵌入(LLE)，然后通过核方法的技巧在高维空间里求解。
The main idea is to approximate the classical local linear embedding (LLE) by introducing a linear transformation matrix and then find the solution in a very high dimensional space by kernel trick.
局部线性嵌入(LLE)算法是有效的非线性降维方法，时间复杂度低并具有强的流形表达能力。
The Locaally linear Embedding (LLE) algorithm is an effective technique for nonlinear dimensionality reduction of high-dimensional data.
局部线性嵌入(LLE)算法是有效的非线性降维方法，时间复杂度低并具有强的流形表达能力。
The Locaally linear Embedding (LLE) algorithm is an effective technique for nonlinear dimensionality reduction of high-dimensional data.
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