线性嵌入 meaning in Chinese
linear imbedding
Examples
- 4 . through the experiments on three different datasets , we illuminate the effective applications of lle and its improvements in the fields of high - dimensional data reduction , visualization and face recognition
4 .通过使用三种不同数据库的仿真实例,探讨了局部线性嵌入及其改进算法在高维数据约简、可视化与人脸识别领域中的应用。 - In section 4 . 2 we analyze its main idea and algorithm in detail , two relevant theorems included ; section 4 . 3 provides plenty instances so to explain its nonlinear dimension reduction ability , section 4 . 4 propose a combined method that integrates the advantage of various methods . in section 4 . 5 we analyze some significant problems in lle , including the locality of manifold representation , the choice of the neighborhood , the intrinsic dimension estimation and the parametric representation of mapping . in section 4 . 6 we design an algorithm for estimating the intrinsic dimension in the base of locally linear approximation and discuss the choice of its parameters
第四章是本文的重点内容，研究一种全新的非线性降维方法? ?局部线性嵌入方法，对它的思想和算法进行了详细的分析，给出算法两个相关定理的证明；第三节对比主成分分析，通过实例说明局部线性嵌入方法的非线性降维特征；第四节在此基础上提出了旨在结合两者优势的组合降维方法；第五节提出了局部线性嵌入方法中存在的若干关键性问题，包括流形的局部性、邻点的选择、本征维数的估计和降维映射的表示，第六节基于局部线性近似的思想提出了一种本征维数的估计方法，设计了实用算法，结合实例对算法中参数的选取进行了讨论；最后一节提出了一种基于局部线性重构的图形分类和识别方法，将其应用于手写体数字的图像分类识别实验，实验得到的分类准确率达96 . 67 。 - This paper deeply studies the manifold learning method called locally linear embedding ( lle ) and improves it . the main achievements in this paper are as follows : 1 . it summarizes the development of manifold learning currently , analyzes the characteristic of nonlinear dimensionality reduction methods , compares the virtues and drawbacks , and makes correlative computer experiments
本文主要对基于流形学习的局部线性嵌入( lle )算法进行了深入的研究与改进,具体工作包括以下四部分: 1 .简要综述了当前流形学习的发展概况,对现有各种非线性降维方法的特点进行分析,比较优点和不足,并进行了相关的计算机仿真实验。