Search - laplacian graph

Search - laplacian graph - List

[Other resource] graph-roberts DL : 0: 本文介绍了边缘检测的基本概念．使用 VC实现了 Roberts算子、Prewitt算子、 Sobel算子和 Laplacian算子．实验分析了各个算子的边缘检测效果及抗噪能力。-This paper introduces the edge detection of the underlying concepts. VC use the Roberts operator, Prewitt operator, Sobel operator and the Laplacian operator. Experimental Analysis of the various operator edge-detection and anti-noise capability.
Date : 2008-10-13 Size : 1mb User : zsc
[Software Engineering] graph-roberts DL : 0: 本文介绍了边缘检测的基本概念．使用 VC实现了 Roberts算子、Prewitt算子、 Sobel算子和 Laplacian算子．实验分析了各个算子的边缘检测效果及抗噪能力。-This paper introduces the edge detection of the underlying concepts. VC use the Roberts operator, Prewitt operator, Sobel operator and the Laplacian operator. Experimental Analysis of the various operator edge-detection and anti-noise capability.
Date : 2025-07-01 Size : 1mb User : zsc
[Mathimatics-Numerical algorithms] NPE DL : 0: 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.
Date : 2025-07-01 Size : 2kb User : 刘国胜
[Mathimatics-Numerical algorithms] radiu DL : 0: 具有固定围长的单圈图的无号拉普拉斯谱半径With fixed long lap around the unsigned graph Laplacian spectral radiu-With fixed long lap around the unsigned graph Laplacian spectral radiu
Date : 2025-07-01 Size : 418kb User : zhou
[matlab] RSE-v1 DL : 0: RSE (Regularized Selective Ensemble) is a selective ensemble learning algorithm for binay classification, which constructs ensemble under the regularization framework. In current version, the graph Laplacian serves as the regularizer, and unlabeled data can also be exploited to improve the performance.
Date : 2025-07-01 Size : 23kb User : 123
[Other] LGME DL : 0: 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
Date : 2025-07-01 Size : 1kb User : Arron
[Algorithm] graf_laplasian DL : 0: laplacian graph algorithm is simple to implement with MATLAB
Date : 2025-07-01 Size : 1kb User : hamed
[2D Graphic] laplacian DL : 0: 基于Laplacian Matrix的图像扩散计算及动画演示程序-This GIF shows the progression of diffusion, as solved by the graph laplacian technique. A graph is constructed over a grid, where each pixel in the graph is connected to its 8 bordering pixels. Values in the image then diffuse smoothly to their neighbors over time via these connections. This particular image starts off with three strong point values which spill over to their neighbors slowly. The whole system eventually settles out to the same value at equilibrium
Date : 2025-07-01 Size : 1kb User : Xiaoping Wang
[Other] LPP DL : 0: lpp算法实现,Locality Preserving Projections局部保持投影算法 hexiaofei的论文(lppLocality Preserving Projections (LPP) builds a graph incorporating neighborhood information of the data set. Using the notion of the Laplacian of the graph, we then compute a transformation matrix which maps the data points to a subspace. This linear transformation optimally preserves local neighborhood information in a certain sense. The representation map generated by the algorithm may be viewed as a linear discrete approximation to a continuous map that naturally arises from the geometry of the manifold .The new algorithm is interesting from a number of perspectives)
Date : 2025-07-01 Size : 1kb User : kazi