# Random projection tree similarity metric for SpectralNet

arXiv: https://arxiv.org/abs/2302.13168 · HTML: https://mlsanity.com/paper/2302.13168/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 32 references that are in the map.
- First posted on arXiv 25 Feb 2023.

## Builds on (most cited first, 9 of 9)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Learning Discrete Structures for Graph Neural Networks](https://mlsanity.com/paper/1903.11960/) (2019)
- [SpectralNet: Spectral Clustering using Deep Neural Networks](https://mlsanity.com/paper/1801.01587/) (2018)
- [K-nearest Neighbor Search by Random Projection Forests](https://mlsanity.com/paper/1812.11689/) (2018)
- [Spectral Clustering via Ensemble Deep Autoencoder Learning (SC-EDAE)](https://mlsanity.com/paper/1901.02291/) (2019)
- [Refining a k-nearest neighbor graph for a computationally efficient spectral clustering](https://mlsanity.com/paper/2302.11296/) (2023)
- [Variational Co-embedding Learning for Attributed Network Clustering](https://mlsanity.com/paper/2104.07295/) (2021)
- [Similarity Kernel and Clustering via Random Projection Forests](https://mlsanity.com/paper/1908.10506/) (2019)

## Abstract (arXiv)

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 k-nn graphs, which are usually constructed using a distance metric (e.g., Euclidean distance). k-nn graphs restrict the points to have a fixed number of neighbors regardless of the local statistics around them. We proposed a new SpectralNet similarity metric based on random projection trees (rpTrees). Our experiments revealed that SpectralNet produces better clustering accuracy using rpTree similarity metric compared to k-nn graph with a distance metric. Also, we found out that rpTree parameters do not affect the clustering accuracy. These parameters include the leaf size and the selection of projection direction. It is computationally efficient to keep the leaf size in order of log(n), and project the points onto a random direction instead of trying to find the direction with the maximum dispersion.