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Description: LGE算法（Linear Graph Embedding）用于降维，代码比较长，比较复杂。供大家研究！-LGE algorithm (Linear Graph Embedding) for dimensionality reduction, code longer, more complicated. For everyone!
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Size: 3072 |
Author: 小哈 |
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Description: 正交的Linear Graph Embedding算法！用于降维，供大家学习交流。-Orthogonal Linear Graph Embedding Algorithm! For dimensionality reduction for them to learn from the exchange.
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Size: 2048 |
Author: 小哈 |
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Description: Semi-Supervised Discriminant Analysis (Graph Embedding Way)
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Size: 15360 |
Author: keyvan |
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Description: 本代码实现基于成对约束的半监督图嵌入算法-Following the intuition that the image variation of
faces can be effectively modeled by low dimensional
linear spaces, we propose a novel linear subspace
learning method for face analysis in the framework of
graph embedding model, called Semi-supervised
Graph Embedding (SGE). This algorithm builds an
adjacency graph which can best respect the geometry
structure inferred from the must-link pairwise
constraints, which specify a pair of instances belong to
the same class. The projections are obtained by
preserving such a graph structure. Using the notion of
graph Laplacian, SGE has a closed solution of an
eigen-problem of some specific Laplacian matrix and
therefore it is quite efficient. Experimental results on
Yale standard face database demonstrate the
effectiveness of our proposed algorithm.
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Size: 2048 |
Author: 刘国胜 |
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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.
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Size: 1024 |
Author: Karthikeyan |
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Description: 半监督鉴别分析是一种很流行的算法，它利用了现实世界的大量的无标记的数据，并对它们分类-Semi-Supervised Discriminant Analysis (Graph Embedding Way)
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Size: 2048 |
Author: 李力 |
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Description: 线性图嵌入方法，该方法是一种基于图框架的子空间学习方法，被用于LPP,NPE等流行学习方法中。-(Regularized) Linear Graph Embedding (Provides a general framework for graph based subspace learning. This function will be called by LPP, NPE, IsoProjection, LSDA, MMP ...)
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Size: 3072 |
Author: caolinlin |
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Description: 关联维数参数（嵌入维数和时间延迟）的计算 。读取txt波形数据，绘制图形，利用CC方法分析图形的的嵌入维数和时间延迟-The correlation dimension parameters (embedding dimension and time delay) calculation. Read txt waveform data, draw pictures using the CC method to analyze the graph of the embedding dimension and time delay
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Size: 1024 |
Author: yihanerhan |
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Description: graph embedding and extensions a general framework for dimensionality
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Size: 1499136 |
Author: 郑 |
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Description: input:
param: parameters of the LMGE algorithm
param.mu, param.alpha, param.beta are regularization parameters.
param.p: dimension of shared subspace
param.k: number of nearest neighbors for Laplacian matrix
X: input data
Y: groundtruth labels
output:
model.W: matrix W
Reference:
Web and Personal Image Annotation by Mining Label Correlation with
Relaxed Visual Graph Embedding
Yi Yang, Fei Wu, Feiping Nie, Heng Tao Shen, Yueting Zhuang and Alex Hauptmann.
contact: yyang@cs.cmu.edu
-Web and Personal Image Annotation by Mining Label Correlation with
Relaxed Visual Graph Embedding
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Size: 1024 |
Author: Arron |
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Description: Linear Graph Embedding
函数形式，可以直接调用-Linear Graph Embedding functional form can be called directly
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Size: 3072 |
Author: yangyang |
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Description: ISOMAP算法，其中做了部分修改。算法采用K近邻图计算测地距离的方法，最后进行低维嵌入-ISOMAP algorithm, which made some modification.Algorithm of geodesic distance is obtained by using the K neighbor graph method, finally to low dimensional embedding
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Size: 2048 |
Author: ee |
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Description: LEM（拉普拉斯特征映射）算法，拉普拉斯特征映射是基于局部邻域，保持局部结构的流形学习方法。LEM通过一个无向加权图刻画流形上数据点间的近邻关系，图的顶点为原始数据点，图的边对应点之间的近邻关系，边的权值对应近邻点之间的相似程度（也可以是某种距离），LEM在低维嵌入空间中尽量保持图中数据点之间的近邻关系，然后求取嵌入坐标。通俗的说，LEM认为在高维数据空间离得近的点在低维嵌入空间也应该离得近-LEM (Laplace feature mapping) algorithm, Laplace feature mapping is based on a local neighborhood, holding manifold learning method local structure. LEM through a non-weighted graph depicts flow to form a neighbor relationship between data points, the graph' s vertex to the original data points, corresponding points edge neighbor relationship graph between the edge weights corresponding to the degree of similarity between neighboring points ( may be some distance), LEM keep neighbor relationship diagram between the data points in a low dimensional embedding space, and then strike embedding coordinates. Popular to say, LEM believes in high-dimensional data space be closer point in the low-dimensional embedding space should be closer
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Size: 482304 |
Author: ccc |
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Description: KGE: Kernel Graph Embedding
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Size: 2048 |
Author: jianglantian |
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