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Description: mani: MANIfold learning demonstration GUI by Todd Wittman, Department of Mathematics, University of Minnesota E-mail wittman@math.umn.edu with comments & questions.
MANI Website: httP://www.math.umn.edu/~wittman/mani/index.html
Last Modified by GUIDE v2.5 10-Apr-2005 13:28:36
Methods obtained from various authors.
(1) MDS -- Michael Lee
(2) ISOMAP -- J. Tenenbaum, de Silva, & Langford
(3) LLE -- Sam Roweis & Lawrence Saul
(4) Hessian LLE -- D. Donoho & C. Grimes
(5) Laplacian -- M. Belkin & P. Niyogi
(6) Diffusion Map -- R. Coifman & S. Lafon
(7) LTSA -- Zhenyue Zhang & Hongyuan Zha
-mani: MANIfold learning demonstration GUI by Todd Wittman, Department of Mathematics, University of Minnesota E-mail wittman@math.umn.edu with comments & questions.
MANI Website: httP://www.math.umn.edu/~wittman/mani/index.html
Last Modified by GUIDE v2.5 10-Apr-2005 13:28:36
Methods obtained from various authors.
(1) MDS-- Michael Lee
(2) ISOMAP-- J. Tenenbaum, de Silva, & Langford
(3) LLE-- Sam Roweis & Lawrence Saul
(4) Hessian LLE-- D. Donoho & C. Grimes
(5) Laplacian-- M. Belkin & P. Niyogi
(6) Diffusion Map-- R. Coifman & S. Lafon
(7) LTSA-- Zhenyue Zhang & Hongyuan Zha
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Size: 14336 |
Author: suxin |
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Description: This function calculates Akaike s final prediction error
％ estimate of the average generalization error.
％
％ [FPE,deff,varest,H] = fpe(NetDef,W1,W2,PHI,Y,trparms) produces the
％ final prediction error estimate (fpe), the effective number of
％ weights in the network if the network has been trained with
％ weight decay, an estimate of the noise variance, and the Gauss-Newton
％ Hessian.
％-This function calculates Akaike s final prediction error estimate of the average generalization error. [FPE, deff, varest, H] = fpe (NetDef, W1, W2, PHI, Y, trparms) produces the final prediction error estimate ( fpe), the effective number of weights in the network if the network has been trained with weight decay, an estimate of the noise variance, and the Gauss-Newton Hessian.
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Size: 2048 |
Author: 张镇 |
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Description: 基于hessian矩阵的图像处理，内附hessian的matlab源码-Hessian matrix-based image processing, Hessian containing the matlab source code
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Size: 1024 |
Author: 郑英杰 |
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Description: 这是一个MATLAB工具箱包括32个降维程序，主要包括 pca,lda,MDS等十几个程序包，对于图像处理非常具有参考价值- ，This Matlab toolbox implements 32 techniques for dimensionality reduction. These techniques are all available through the COMPUTE_MAPPING function or trhough the GUI. The following techniques are available:
- Principal Component Analysis ( PCA )
- Linear Discriminant Analysis ( LDA )
- Multidimensional scaling ( MDS )
- Probabilistic PCA ( ProbPCA )
- Factor analysis ( FactorAnalysis )
- Sammon mapping ( Sammon )
- Isomap ( Isomap )
- Landmark Isomap ( LandmarkIsomap )
- Locally Linear Embedding ( LLE )
- Laplacian Eigenmaps ( Laplacian )
- Hessian LLE ( HessianLLE )
- Local Tangent Space Alignment ( LTSA )
- Diffusion maps ( DiffusionMaps )
- Kernel PCA ( KernelPCA )
- Generalized Discriminant Analysis ( KernelLDA )
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Size: 1108992 |
Author: yang |
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Description: hessian程序，可用于增强算法的研究-hessian program
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Size: 12288 |
Author: dd |
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Description: matlab code for image processing
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Size: 8192 |
Author: nimmi |
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Description: matlab code for image processing
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Size: 1024 |
Author: nimmi |
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Description: Semi-automatic Differentiation (SD) Toolkit is a Matlab implementation
of the complex step derivative (CSD) technique for the differentiation of real-valued functions. The Toolkit consists of three core functions:
sdGrad.m - Returns CSD approximation of the gradient (g) of the
scalar-valued target function fun(p,Extra), according to Equation 3
of the paper.
sdJac.m - Returns CSD approximation of the Jacobian (J) of the
scalar-valued target function fun(p,Extra), according to Equation 3
of the paper.
sdHg.m - Rerurns CSD approximation of the Hessian (H) of the
scalar-valued target function fun(p,Extra), according to Equation 7 of the
paper. It also returns the centered-difference CSD approximation of the
gradient as a by-product.
For a brief describtion of the functions in the toolkit,
type ,help sdToolkit> at Matlab command prompt.-The sdToolkit demonstrates the complex step derivative method on a variety of functions and geophysically oriented examples
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Size: 28672 |
Author: 蒋礼 |
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Description: This document describe how to extract keypoints form pictures. Method used - harris affine and hessian affine
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Size: 2582528 |
Author: Jakub |
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Description: Hessian矩阵计算，在matlab下开发，二维三维-Hessian matrix calculations, developed under matlab, two-dimensional three-dimensional
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Size: 4072448 |
Author: su |
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Description: 图像的HESSIAN矩阵特征图像，从而可以针对特征值进行对图像增强-Characteristics of the image HESSIAN matrix image, which can target value of image enhancement features
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Size: 275456 |
Author: zzz |
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Description: 基于hessian矩阵的图像处理，，内附hessian的matlab源码
-Hessian matrix-based image processingg, matlab source containing the hessian
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Size: 1024 |
Author: 藐视 |
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Description: 多种流行学习算法的MATLAB实现。包括MDS、ISOMAP、 LLE 、 Hessian LLE 、Laplacian、LTSA 等多种算法。-A variety of popular learning algorithm in MATLAB to achieve. MDS, ISOMAP, LLE, Hessian LLE, Laplacian, LTSA.
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Size: 15360 |
Author: 刘娇月 |
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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
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Size: 2029568 |
Author: jdzsj |
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Description: Calculate the eigenvalues of many 3x3 real symmetric matrices.-Calculate the eigenvalues of many 3x3 real symmetric matrices. Computation is non-iterative, based on fully vectorized matlab matrix operations, and GPU computation is supported. It is fast and efficient for processing a number of 3-by-3 matrices at once. This code particularly suits tensor/Riemannian calculus in 3D, visualization/analysis of volumetric tensor images, image Jacobian/Hessian analysis, stress/tensile strength computation on tensor fields, three dimensional diffusion processes, determining the rotation axes of a motion field, etc....
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Size: 1024 |
Author: naud |
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Description: hessian matlab algorithm
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Size: 1309696 |
Author: amtcorporation
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Description: 用matlab写的steger算法，用于结构光条提取(Steger algorithm written in MATLAB for structural light strip extraction)
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Size: 66560 |
Author: 来风
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Description: 可利用matlab绘制海森概率格点图，并可依据自身要求，自主设置格点位置(Draw the Hessian probability map)
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Size: 1024 |
Author: szk222
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Description: Frangi最初提出的计算Hessian矩阵，计算特征值，构造血管增强函数，进行血管增强，Matlab代码，可直接运行。特征值计算为c语言代码，容易将整个算法转化为C或C++，非常有用。(Frangi was first proposed to calculate the Hessian matrix, to calculate the eigenvalues, to construct the vascular enhancement function, to carry out the vascular enhancement, and to run the Matlab code. The feature value is calculated as C language code, and it is very useful to convert the whole algorithm into C or C++.)
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Size: 4069376 |
Author: `想起
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Description: 使用海森矩阵，根据特征值，特征向量分割一些特征明显的目标(For Hessian matrix analysis)
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Size: 1024 |
Author: Garthf |
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