共被期刊论文引用了4次
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| 1 | 多维数据的聚类结果可视化技术综述显示文摘在很多情况下,人们不仅需要聚类算法给出类标,还需要掌握聚类结构和数据分布情况.为满足后一项需求,出现了许多聚类结果的可视化(简称聚类可视化)技术,以图形的方式将多维数据和其聚类结果显示在二维或三维空间.从直接在二维或三维空间显示数据及其聚类结果、数据经降维(或映射)后显示以及其它显示方式3种角度综述了常用的30多种聚类可视化方法,并对各种方法的优缺点和适用性进行了分析和讨论. | 王开军 | 2012 | 福建师范大学学报(自然科学版)2012，28，4： | 5 |
| 2 | 基于降维的聚类可视化技术显示文摘基于降维或映射技术的聚类结果可视化技术提供了在二维或三维空间直观地分析数据集的聚类结构、聚类质量和分布信息的有效手段.对线性降维可视化方法、非线性降维可视化方法及映射可视化方法等进行了介绍、实例展示和讨论分析,最后对这类方法的优缺点、存在的问题和进一步的研究方向做了总结和展望. | 王开军 | 2011 | 福建师范大学学报(自然科学版)2011，27，4： | 4 |
| 3 | Image feature optimization based on nonlinear dimensionality reduction显示文摘Image feature optimization is an important means to deal with high-dimensional image data in image semantic understanding and its applications. We formulate image feature optimization as the establishment of a mapping between highand low-dimensional space via a five-tuple model. Nonlinear dimensionality reduction based on manifold learning provides a feasible way for solving such a problem. We propose a novel globular neighborhood based locally linear embedding (GNLLE) algorithm using neighborhood update and an incremental neighbor search scheme, which not only can handle sparse datasets but also has strong anti-noise capability and good topological stability. Given that the distance measure adopted in nonlinear dimensionality reduction is usually based on pairwise similarity calculation, we also present a globular neighborhood and path clustering based locally linear embedding (GNPCLLE) algorithm based on path-based clustering. Due to its full consideration of correlations between image data, GNPCLLE can eliminate the distortion of the overall topological structure within the dataset on the manifold. Experimental results on two image sets show the effectiveness and efficiency of the proposed algorithms. | Rong ZHU Min YAO | 2009 | Journal of Zhejiang University-Science A(Applied Physics & Engineering)2009，10，12： | 3 |
| 4 | Atlas Compatibility Transformation:A Normal Manifold Learning Algorithm显示文摘Over the past few years,nonlinear manifold learning has been widely exploited in data analysis and machine learning.This paper presents a novel manifold learning algorithm,named atlas compatibility transformation(ACT),It solves two problems which correspond to two key points in the manifold definition:how to chart a given manifold and how to align the patches to a global coordinate space based on compatibility.For the first problem,we divide the manifold into maximal linear patch(MLP) based on normal vector field of the manifold.For the second problem,we align patches into an optimal global system by solving a generalized eigenvalue problem.Compared with the traditional method,the ACT could deal with noise datasets and fragment datasets.Moreover,the mappings between high dimensional space and low dimensional space are given.Experiments on both synthetic data and real-world data indicate the effection of the proposed algorithm. | Zhong-Hua Hao Shi-Wei Ma Fan Zhao | 2015 | International Journal of Automation and computing2015，12，4： | 0 |
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