搜索资源列表
NPE
- 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 embeddi
LGE
- 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!
OLPP
- 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.
SDA
- Semi-Supervised Discriminant Analysis (Graph Embedding Way)
isomap
- 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-is
SDA
- 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)
LGE
- 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 ...)
C_one
- 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 dimensio
LGME
- 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: ground
LGE.m
- Linear Graph Embedding 函数形式，可以直接调用-Linear Graph Embedding functional form can be called directly
ISOMAP-Algorithm
- 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
LEM-Algorithm
- 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 fea
KGE
- KGE: Kernel Graph Embedding
Graph-regularized low-rank-embedding
- 一组关于图正则的低秩嵌入算法的数值解法，可直接使用。