3 results about "Laplacian matrix" patented technology

The invention relates to a dynamic subgraph generation method based on node features and reply paths, and a dispute detection method. The dynamic subgraph generation methodincludes the following steps: S1, constructing a path matrix P and a path length matrix S based on a post-comment graph G, wherein the path matrix P records all paths from each node to a terminal node in the graph G, the terminal nodes comprise post nodes in the graph G and comment nodes without replies, and the path length matrix S records the length of each path in the path matrix; s2, calculating a path Laplacian matrix L based on the path matrix P and the path length matrix S; s3, on the basis of the path Laplacian matrix L and the content features of the nodes in the graph G, calculating and obtaining the expression of the current node sensing path information; and S4, on the basis of the similarity between the current node and all nodes on the corresponding path, and reserving the most important part of nodes on each path, wherein the important nodes on all the paths form a sub-graph corresponding to the current node, and the nodes in the sub-graph are local discussion related to the current node.

The invention discloses a point cloud data segmentation method based on spectral clustering . The method comprises the following steps of reading an input point cloud data set and a clustering number;normalizing the coordinates of the point cloud data set to obtain normalized coordinates; constructing a similar matrix by normalizing the coordinates, calculating a Laplacian matrix Lrw, and calculating minimum m + 2 eigenvalues and eigenvectors f corresponding to the minimum m + 2 eigenvalues; standardizing a matrix formed by the feature vectors f according to rows to obtain a feature matrix F;taking each row in the feature matrix F as an m + 2-dimensional sample, obtaining m clusters from the samples by using a K-means clustering method, and outputting the m clusters; the method solves aproblem that a conventional point cloud segmentation result is not fine enough, maintains the invariance of the segmentation result for the translation, rotation and zooming of the point cloud data, and is also suitable for point cloud data or sparse data with non-uniform density.