Description: MULTIDIMENSIONAL SCALING in matlab by Mark Steyvers 1999
％needs optimization toolbox
％Modified by Bruce Land
％--Data via globals to anaylsis programs
％--3D plotting with color coded groups
％--Mapping of MDS space to spike train temporal profiles as described in
％Aronov, et.al. \"Neural coding of spatial phase in V1 of the Macaque\" in
％press J. Neurophysiology-MULTIDIMENSIONAL SCALING in Matlab by Mar 1999% k Steyvers needs optimization toolbox% M odified by Bruce Land% -- Data via globals to ana ylsis programs% -- 3D plotting with color coded groups% -- Mapping of MDS space to spike train te mporal profiles as described in% Aronov, et.al. "Neural coding of spatial phase in V1 of t he Macaque "in press J. Neurophysiology% Platform: |
Size: 2288 |
Author:左贤君 |
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Description: MULTIDIMENSIONAL SCALING in matlab by Mark Steyvers 1999
％needs optimization toolbox
％Modified by Bruce Land
％--Data via globals to anaylsis programs
％--3D plotting with color coded groups
％--Mapping of MDS space to spike train temporal profiles as described in
％Aronov, et.al. "Neural coding of spatial phase in V1 of the Macaque" in
％press J. Neurophysiology-MULTIDIMENSIONAL SCALING in Matlab by Mar 1999% k Steyvers needs optimization toolbox% M odified by Bruce Land%-- Data via globals to ana ylsis programs%-- 3D plotting with color coded groups%-- Mapping of MDS space to spike train te mporal profiles as described in% Aronov, et.al. "Neural coding of spatial phase in V1 of t he Macaque "in press J. Neurophysiology% Platform: |
Size: 2048 |
Author:左贤君 |
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Description: This codes include MDS code for communication and other uses.
Multidimensional scaling (MDS) is a set of related statistical techniques often used in formation visualization for exploring similarities or dissimilarities in data. MDS is a special case of ordination. An MDS algorithm starts with a matrix of item–item similarities, then assigns a location to each item in N-dimensional space, where N is specified a priori. For sufficiently small N, the resulting locations may be displayed in a graph or 3D visualisation.-This codes include MDS code for communication and other uses.
Multidimensional scaling (MDS) is a set of related statistical techniques often used in information visualization for exploring similarities or dissimilarities in data. MDS is a special case of ordination. An MDS algorithm starts with a matrix of item–item similarities, then assigns a location to each item in N-dimensional space, where N is specified a priori. For sufficiently small N, the resulting locations may be displayed in a graph or 3D visualisation. Platform: |
Size: 19456 |
Author:weihuagao |
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Description: Estimating the positions of sensor nodes is a fundamental and
crucial problem in ad hoc wireless sensor networks (WSNs).
In this paper, an accurate node localization method for WSNs
is devised based on the weighted least squares technique with
the use of time-of-arrival measurements. Computer simulations
are included to evaluate the performance of the proposed
approach by comparing with the classical multidimensional
scaling method and Cram´ er-Rao lower bound. Platform: |
Size: 518144 |
Author:pravin jadhav |
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Description: In statistics, Isomap is one of several widely used low-dimensional embedding methods, where geodesic distances on a weighted graph are incorporated with the classical scaling (metric multidimensional scaling). Isomap is used for computing a quasi-isometric, low-dimensional embedding of a set of high-dimensional data points. The algorithm provides a simple method for estimating the intrinsic geometry of a data manifold based on a rough estimate of each data point’s neighbors on the manifold. Isomap is highly efficient and generally applicable to a broad range of data sources and dimensionalities. Platform: |
Size: 1024 |
Author:Karthikeyan |
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Description: Multidimensional scaling covers a variety of techniques, with its
main development having rested in the hands of mathematical psychologists
and the journal Psychometrika having championed the
publication of articles in the subject. Multidimensional scaling has
now become popular and has extended into areas other than its
traditional place in the behavioural sciences. Many statistical computer
packages now include multidimensional scaling. Platform: |
Size: 9875456 |
Author:Azarm |
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Description: 本文提出一种基于多维定标的无线传感器网络三维定位算法,结合RSS经验衰减模型和最短路径建立相异性矩阵,采用轻量级矩阵分解算法降低相异性矩阵分解的计算复杂性,并利用网络中存在的周期性消息将初始定位信息回送,在后台使用迭代优化算法对初始定位结果求精。仿真实验表明,在测距误差一定的情况下,该算法能够提高节点三维坐标的初始计算精度,经过集中式的优化求精后与MDS-MAP算法相比,能够明显地提高节点三维定位的精度-This paper presents a method based on multidimensional scaling in wireless sensor network positioning algorithm, combined with RSS experience attenuation model and the shortest path to establish dissimilarity matrix, using lightweight matrix factorization algorithm reduces computational complexity dissimilarity matrix decomposition, and the use of the network in the presence of periodic messages will be the initial positioning information return, in the background using an iterative optimization the algorithm to the initial positioning results refinement. Simulation results show that, in some cases ranging error, this algorithm can improve the calculation accuracy of three-dimensional coordinates of the initial node, through the centralized optimization refinement after compared with MDS-MAP algorithm, which can obviously improve the precision of 3D Node Localization Platform: |
Size: 185344 |
Author:于文娟 |
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Description: C++与FORTRAN混编程序。考虑各向异性的克里金法及序贯高斯法。在地质，油藏建模中很有用处
Author--Jeff Boisvert and Clayton V. Deutsch-for incorporating locally varying anisotropy in kriging or sequential Gaussian simulation is based on modifying how locations in space are related. Normally, the straight line path is used however, when nonlinear features exist the appropriate path between locations follows along the features. The Dijkstra algorithm is used to determine the shortest path/distance between locations and a conventional covariance or variogram function is used. This nonlinear path is a non-Euclidean distance metric and positive definiteness of the resulting kriging system of equations is not guaranteed. Multidimensional scaling (landmark isometric mapping) is used to ensure positive definiteness. Platform: |
Size: 467968 |
Author:张开 |
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Description: ISOMAP(Isometric Feature Mapping，等度规特征映射)算法在高维非线性数据处理中有较为理想的效果，建立在MDS (Multi-Dimensional Scaling，多维尺度变换)的基础上，其基本思想是当数据集的分布具有低维嵌入流形结构时，可以通过保距映射获得观测空间数据集在低维空间的表示。-ISOMAP (Isometric Feature Mapping, and other metric feature mapping) algorithm can get more satisfactory results in high-dimensional nonlinear data processing, based on the MDS (Multi-Dimensional Scaling, multidimensional scaling), based on the basic idea is that when the data set distribution of low dimensional embedding manifold structure, you can get insurance the observation of spatial data sets mapped low dimensional space of representation . Platform: |
Size: 3072 |
Author:ct |
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Description: 本代码仿真标签定位的原理，采用的技术是多维标度，里面包含了信道建立以及最小二乘法的使用-The principle of the code simulation label positioning, the use of the technology is multidimensional scaling, which contains the use of the channel and the establishment of the method of least squares Platform: |
Size: 17408 |
Author:刘伟伟 |
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Description: Multidimensional Scaling(MDS)是一种经典的数据降维方法，同时也是数据可视化的一种手段。这个问题的最早起源，是当我们仅能获得物体之间的相似性矩阵时，如何由此来重构它们的欧几里德坐标。譬如，对一个国家的许多城市而言，假如我们并不能确定它们的经纬度信息，却知道所有城市两两之间的距离，就可以通过MDS方法将这些代表相似性的距离数据，呈现在二维坐标上。-Multidimensional Scaling (MDS) is a classical data dimensionality reduction method, and it is also a means of data visualization. The earliest origin of this problem is how to reconstruct Euclidean coordinates when we can only obtain the similarity matrix between objects. For example, for many cities in a country, if we can not determine their latitude and longitude information, but know the distance between all cities, you can MDS method to represent these similarity distance data, presented in two-dimensional Coordinate. Platform: |
Size: 4165632 |
Author:张立晔 |
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Search - multidimensional scaling