44 citations · 58 across the 6 of their papers we have counts for
6 papers
Random projection tree similarity metric for SpectralNet
Mashaan Alshammari, John Stavrakakis, Adel F. Ahmed +1
SpectralNet is a graph clustering method that uses neural network to find an embedding that separates the data. So far it was only used with -nn graphs, which are usually constr…
A parameter-free graph reduction for spectral clustering and SpectralNet
Mashaan Alshammari, John Stavrakakis, Masahiro Takatsuka
Graph-based clustering methods like spectral clustering and SpectralNet are very efficient in detecting clusters of non-convex shapes. Unlike the popular -means, graph-based clu…
The Effect of Points Dispersion on the -nn Search in Random Projection Forests
Mashaan Alshammari, John Stavrakakis, Adel F. Ahmed +1
Partitioning trees are efficient data structures for -nearest neighbor search. Machine learning libraries commonly use a special type of partitioning trees called d-trees to…
Refining a -nearest neighbor graph for a computationally efficient spectral clustering
Mashaan Alshammari, John Stavrakakis, Masahiro Takatsuka
Spectral clustering became a popular choice for data clustering for its ability of uncovering clusters of different shapes. However, it is not always preferable over other clusteri…
Random Projection Forest Initialization for Graph Convolutional Networks
Mashaan Alshammari, John Stavrakakis, Adel F. Ahmed +1
Graph convolutional networks (GCNs) were a great step towards extending deep learning to unstructured data such as graphs. But GCNs still need a constructed graph to work with. To…
Graph Construction using Principal Axis Trees for Simple Graph Convolution
Mashaan Alshammari, John Stavrakakis, Adel F. Ahmed +1
Graph Neural Networks (GNNs) are increasingly becoming the favorite method for graph learning. They exploit the semi-supervised nature of deep learning, and they bypass computation…