Location:
Search - MVU
Search list
Description: 流形学习中的重要方法MVU的源代码，也就是所谓的sde
Platform: |
Size: 8615764 |
Author: gxf |
Hits:
Description: 一种针对流形学习算法LLE的改进算法介绍，采用它有利于提高流形学习算法降低噪声的干扰。
Platform: |
Size: 5120 |
Author: 罗朝辉 |
Hits:
Description: 流形学习中的重要方法MVU的源代码，也就是所谓的sde-Manifold learning an important means of MVU
Platform: |
Size: 8615936 |
Author: gxf |
Hits:
Description: Fei Sha 等人编写的流形学习算法CCA的matlab代码，它基于MVU算法，但是计算速度比较慢-Fei Sha and others prepared CCA manifold learning algorithm of matlab code, which is based on MVU algorithm, but the calculation speed is relatively slow
Platform: |
Size: 16384 |
Author: Chenping Hou |
Hits:
Description: 基于matlab开发的一个简单的流形学习的工具箱，附带有使用说明-Matlab developed based on a simple manifold learning kit comes with instructions
Platform: |
Size: 245760 |
Author: 喻军 |
Hits:
Description: MVU算法的详细分析，标准的分类算法，高效实现分类-MVU
Platform: |
Size: 8609792 |
Author: anelka |
Hits:
Description: 一个利用半正定规划求解 SDE/MVU 非线性数据降维的算法实现，这是论文原作者提供的 MATLAB 代码。-A MATLAB implementation of the Semi-Definite Embedding (SDE) or namely Maximum Variance Unfolding (MVU) algorithm, provided by the author himself.
Platform: |
Size: 17408 |
Author: bsmyht |
Hits:
Description: matlab 降维工具箱，最新版本。包含各类线性及非线性降维代码，lle,lpp,mvu,isomap,npe等皆在其中。-DRTOOL, by itself, creates a new DRTOOL or raises the existing
singleton*.
H = DRTOOL returns the handle to a new DRTOOL or the handle to
the existing singleton*.
DRTOOL( CALLBACK ,hObject,eventData,handles,...) calls the local
function named CALLBACK in DRTOOL.M with the given input arguments.
DRTOOL( Property , Value ,...) creates a new DRTOOL or raises the
existing singleton*. Starting from the left, property value pairs are
applied to the GUI before drtool_OpeningFunction gets called. An
unrecognized property name or invalid value makes property application
stop. All inputs are passed to drtool_OpeningFcn via varargin.
*See GUI Options on GUIDE s Tools menu. Choose "GUI allows only one
instance to run (singleton)".
Platform: |
Size: 1952768 |
Author: lu |
Hits:
Description: Matlab针对各种数据预处理的降维方法，源码集合。-Currently, the Matlab Toolbox for Dimensionality Reduction contains the following techniques:
Principal Component Analysis (PCA)
Probabilistic PCA
Factor Analysis (FA)
Sammon mapping
Linear Discriminant Analysis (LDA)
Multidimensional scaling (MDS)
Isomap
Landmark Isomap
Local Linear Embedding (LLE)
Laplacian Eigenmaps
Hessian LLE
Local Tangent Space Alignment (LTSA)
Conformal Eigenmaps (extension of LLE)
Maximum Variance Unfolding (extension of LLE)
Landmark MVU (LandmarkMVU)
Fast Maximum Variance Unfolding (FastMVU)
Kernel PCA
Generalized Discriminant Analysis (GDA)
Diffusion maps
Stochastic Neighbor Embedding (SNE)
Symmetric SNE (SymSNE)
new: t-Distributed Stochastic Neighbor Embedding (t-SNE)
Neighborhood Preserving Embedding (NPE)
Locality Preserving Projection (LPP)
Linear Local Tangent Space Alignment (LLTSA)
Stochastic Proximity Embedding (SPE)
Mu
Platform: |
Size: 2029568 |
Author: jdzsj |
Hits:
Description: 流形学习中的重要方法MVU的源代码，也就是所谓的sde-Manifold learning an important means of MVU-MVU
Platform: |
Size: 8615936 |
Author: LDA |
Hits:
Description: Minimum Variance Unbiased Estimation
Platform: |
Size: 1187840 |
Author: sh |
Hits:
Description: This routine calculates the MVU estimator and plots the Cramer_Rao Lower Bound in estimating the Average of Gausian Noise (i.e. A)assuming that the variance of the noise is variable between 1 to 5 and also the number of ndependent data samples is varying 10~100 with the step of 10
Platform: |
Size: 2048 |
Author: sh |
Hits:
Description: 多径信道的mvu和mmse估计，并统计器错误概率-Mvu and mmse multi-path channel estimation and statistical error probability
Platform: |
Size: 2048 |
Author: lin |
Hits:
Description: brief Demo code to calculate moments
Platform: |
Size: 12571648 |
Author: 刘康 |
Hits:
Description: 信道估计,we will assume that the receiver knows your name, i.e., it knows s (and of course
x). Based on this knowledge, the receiver will estimate h using the MVU and MMSE method
(for the latter it is also assumed that the variance of the noise σ2 is known at the receiver).-Channel Estimation:we will assume that the receiver knows your name, i.e., it knows s (and of course
x). Based on this knowledge, the receiver will estimate h using the MVU and MMSE method
(for the latter it is also assumed that the variance of the noise σ2 is known at the receiver).
Platform: |
Size: 6144 |
Author: lauren |
Hits:
Description: This paper is concerned with the minimum variance unbiased (MVU) finite impulse response (FIR)
filtering problem for linear system described by discrete time-variant state-space models. An MVU FIR
filter is derived by minimizing the variance the unbiased FIR (UFIR) filter. The relationship between
the filter gains of MVU FIR, UFIR and optimal FIR (OFIR) filters is derived analytically, and the mean square
errors (MSEs) of different FIR filters are compared to provide an insight into the estimation performance.
Simulations provided verify that errors in the MVU FIR filter are in between the UFIR and OFIR filters. It
is also shown that the MVU FIR filter can offer optimal estimates without a prior knowledge of the initial
state, and exhibits better robustness against temporary modeling uncertainties than the Kalman filter.-This paper is concerned with the minimum variance unbiased (MVU) finite impulse response (FIR)
filtering problem for linear system described by discrete time-variant state-space models. An MVU FIR
filter is derived by minimizing the variance the unbiased FIR (UFIR) filter. The relationship between
the filter gains of MVU FIR, UFIR and optimal FIR (OFIR) filters is derived analytically, and the mean square
errors (MSEs) of different FIR filters are compared to provide an insight into the estimation performance.
Simulations provided verify that errors in the MVU FIR filter are in between the UFIR and OFIR filters. It
is also shown that the MVU FIR filter can offer optimal estimates without a prior knowledge of the initial
state, and exhibits better robustness against temporary modeling uncertainties than the Kalman filter.
Platform: |
Size: 925696 |
Author: 杨松 |
Hits:
Description: 最大差伸展算法的一些算法和相应的代码，对于想了解MVU算法的改进与应用，有重要作用-Biggest difference stretching algorithm of some algorithms and the corresponding code, to want to know the improvement and application of MVU algorithm play an important role
Platform: |
Size: 3353600 |
Author: 赵礼 |
Hits:
Description: Fei Sha 等人编写的流形学习算法CCA的matlab代码,它基于MVU算法,但是计算速度比较慢()
Platform: |
Size: 11264 |
Author: weresa |
Hits:
Description: 拉普拉斯特征映射,最大差异展开，时频域特征(Laplacian Eigenmap Maximum difference expansion Fast Maximum difference expansion ISOMAP)
Platform: |
Size: 8192 |
Author: chen_1001 |
Hits:
Description: Principal Component Analysis (PCA)
Probabilistic PCA
Factor Analysis (FA)
Sammon mapping
Linear Discriminant Analysis (LDA)
Multidimensional scaling (MDS)
Isomap
Landmark Isomap
Local Linear Embedding (LLE)
Laplacian Eigenmaps
Hessian LLE
Local Tangent Space Alignment (LTSA)
Conformal Eigenmaps (extension of LLE)
Maximum Variance Unfolding (extension of LLE)
Landmark MVU (LandmarkMVU)
Fast Maximum Variance Unfolding (FastMVU)
Platform: |
Size: 1003143 |
Author: 401116575@qq.com |
Hits: