44 citations · 74 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…
Approximate spectral clustering density-based similarity for noisy datasets
Mashaan Alshammari, Masahiro Takatsuka
Approximate spectral clustering (ASC) was developed to overcome heavy computational demands of spectral clustering (SC). It maintains SC ability in predicting non-convex clusters.…
Approximate spectral clustering with eigenvector selection and self-tuned
Mashaan Alshammari, Masahiro Takatsuka
The recently emerged spectral clustering surpasses conventional clustering methods by detecting clusters of any shape without the convexity assumption. Unfortunately, with a comput…
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…