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Description: lle和svm 的人脸识别算法代码，识别率可以打到80以上。
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Size: 1203 |
Author: 郭锋 |
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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。
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Size: 5523 |
Author: 罗朝辉 |
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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
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Size: 14212 |
Author: suxin |
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Description: 包含了大多数流形学习方法的代码,有PCA,ISOMAP,LLE,HLLE
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Size: 14258 |
Author: gxf |
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Description: （PCA LDA LLE）各种降维方法Matlab工具箱
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Size: 1121411 |
Author: wang3096369@126.com |
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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。
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Size: 5120 |
Author: 罗朝辉 |
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Description:
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Size: 2048 |
Author: liley |
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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
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Size: 245760 |
Author: 喻军 |
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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:
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Size: 2048 |
Author: Owen |
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Description: 多种信号过完备字典学习算法的工具包，包含文献Surveying and comparing simultaneous sparse approximation (or group-lasso) algorithms中所有的算法。-Multiple signals over-complete dictionary learning algorithm toolkit, including literature Surveying and comparing simultaneous sparse approximation (or group-lasso) algorithms for all algorithms.
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Size: 13312 |
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Description: 流形学习是近年来机器学习及模式识别等领域的一个研究热点，其主要目标是去发现高维观察数据空间的低维光滑流形。自从2000年Roweis和Saul提出LLE算法、Tenenbaum等人提出Isomap算法，特别是Donoho等人发现Isomap算法能够准确发现人脸图像流形潜在的参数空间、张长水等人将LLE算法用于人脸识别并取得了较好的识别效果之后，基于流形学习的人脸识别研究引起了人们的广泛关注。-Manifold learning in recent years the field of machine learning and pattern recognition as a research focus, its main goal is to find high-dimensional data space observation of low-dimensional smooth manifold. Since 2000 LLE algorithm proposed by Roweis and Saul, Tenenbaum and others proposed Isomap algorithm, especially Donoho, who found that Isomap algorithm can find the potential face images of the parameter space manifold, and Chang-water will LLE algorithm for face recognition and to obtain a good recognition results, the manifold learning-based face recognition research has aroused extensive attention.
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Size: 15360 |
Author: 韩静书 |
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Description: 常见的流形学习方法LDA和LLE,此程序经过调试运行！-Manifold learning methods common LDA and LLE, this program after commissioning!
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Size: 2048 |
Author: shi |
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Description: wilson活度系数模型,化工领域，主要应用是求取液相活度系数，但仅仅能用于LLE计算。-wilson activity model
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Size: 1024 |
Author: Dino |
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Description: 基于多帧图像插值（Interpolation）技术的方法是SR恢复技术当中最直观
的方法。这类方法首先估计各帧图像之间的相对运动信息，获得HR图像在非均
匀间距采样点上的象素值，接着通过非均匀插值得到HR栅格上的象素值，最后
采用图像恢复技术来去除模糊和降低噪声（运动估计!非均匀插值!去模糊和
噪声）。-In this paper, we propose a novel method for solv-
ing single-image super-resolution problems. Given a
low-resolution image as input, we recover its high-
resolution counterpart using a set of training exam-
ples. While this formulation resembles other learning-
based methods for super-resolution, our method has
been inspired by recent manifold learning methods, par-
ticularly locally linear embedding (LLE). Speci?cally,
small image patches in the low- and high-resolution
images form manifolds with similar local geometry in
two distinct feature spaces. As in LLE, local geometry
is characterized by how a feature vector correspond-
ing to a patch can be reconstructed by its neighbors
in the feature space. Besides using the training image
pairs to estimate the high-resolution embedding, we
also enforce local compatibility and smoothness con-
straints between patches in the target high-resolution
image through overlapping. Experiments show that our
method is very ?exible
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Size: 27595776 |
Author: qianyeyu |
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Description: 实现了基于Gabor和LLE的人脸识别，在ORL数据库上有较好的效果-The code implement the facerecognition based on Gabor feature and LLE dimension reduction.
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Size: 2048 |
Author: 韩静书 |
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Description: 维数约减matlab工具箱，包括LLE，ISOMAP，NPE等，具有较好的效果-Dimensionality reduction matlab toolbox, including LLE, ISOMAP, NPE, etc., with good results
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Size: 936960 |
Author: 韩静书 |
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Description: INPUTES:
y: y is vector of values(time series data)
tau: embedding lag of state space reconstruction. When you have not
any information about tau please let it zero. The code will calculates
the tau.
m: m is embedding dimension. If you have not any information about
embedding dimension please let it zero. the code will find proper
embedding dimension.
OUTPUTS:
LLE: Largest Lyapunov Exponent
lambda: Lyapunov exponents for various ks. Plot of this exponents is
very helpful. If embedding dimension be selected correctly lambda curve
will have smooth part(or fairly horizontal). If there is no smooth
section on the curve, it is better you try with other embedding
dimensions.- INPUTES:
y: y is vector of values(time series data)
tau: embedding lag of state space reconstruction. When you have not
any information about tau please let it zero. The code will calculates
the tau.
m: m is embedding dimension. If you have not any information about
embedding dimension please let it zero. the code will find proper
embedding dimension.
OUTPUTS:
LLE: Largest Lyapunov Exponent
lambda: Lyapunov exponents for various ks. Plot of this exponents is
very helpful. If embedding dimension be selected correctly lambda curve
will have smooth part(or fairly horizontal). If there is no smooth
section on the curve, it is better you try with other embedding
dimensions.
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Size: 4096 |
Author: Hesham |
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