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Found 10 papers, 10 papers with code
The Effect of Points Dispersion on the $k$-nn Search in Random Projection Forests
$k$-nn search in an rpForest is influenced by two factors: 1) the dispersion of points along the random direction and 2) the number of rpTrees in the rpForest.
Random projection tree similarity metric for SpectralNet
Our experiments revealed that SpectralNet produces better clustering accuracy using rpTree similarity metric compared to $k$-nn graph with a distance metric.
A parameter-free graph reduction for spectral clustering and SpectralNet
We introduce a graph reduction method that does not require any parameters.
Approximate spectral clustering density-based similarity for noisy datasets
Also, CONN could be tricked by noisy density between clusters.
Approximate spectral clustering with eigenvector selection and self-tuned $k$
The recently emerged spectral clustering surpasses conventional clustering methods by detecting clusters of any shape without the convexity assumption.
Refining a $k$-nearest neighbor graph for a computationally efficient spectral clustering
We proposed a refined version of $k$-nearest neighbor graph, in which we keep data points and aggressively reduce number of edges for computational efficiency.
Graph Construction using Principal Axis Trees for Simple Graph Convolution
We introduce a graph construction scheme that constructs the adjacency matrix $A$ using unsupervised and supervised information.
Random Projection Forest Initialization for Graph Convolutional Networks
In a $k$-nn graph, points are restricted to have a fixed number of edges, and all edges in the graph have equal weights.
The Effect of Points Dispersion on the k-nn Search in Random Projection Forests
k -nn search in an rpForest is influenced by two factors: 1) the dispersion of points along the random direction and 2) the number of rpTrees in the rpForest.
Refining a -nearest neighbor graph for a computationally efficient spectral clustering
We proposed a refined version of -nearest neighbor graph, in which we keep data points and aggressively reduce number of edges for computational efficiency.
