# DiSC: Differential Spectral Clustering of Features

arXiv: https://arxiv.org/abs/2211.05314 · HTML: https://mlsanity.com/paper/2211.05314/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 53 references that are in the map.
- First posted on arXiv 10 Nov 2022.

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

- [Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks](https://mlsanity.com/paper/1810.08468/) (2018)
- [Exact Recovery in the Stochastic Block Model](https://mlsanity.com/paper/1405.3267/) (2014)
- [Feature Selection using Stochastic Gates](https://mlsanity.com/paper/1810.04247/) (2018)
- [Recovering Hidden Components in Multimodal Data with Composite Diffusion Operators](https://mlsanity.com/paper/1808.07312/) (2018)
- [C-SALT: Mining Class-Specific ALTerations in Boolean Matrix Factorization](https://mlsanity.com/paper/1906.09907/) (2019)
- [Graph-based regularization for regression problems with alignment and highly-correlated designs](https://mlsanity.com/paper/1803.07658/) (2018)
- [Non-Redundant Spectral Dimensionality Reduction](https://mlsanity.com/paper/1612.03412/) (2016)
- [Alternating Diffusion Map Based Fusion of Multimodal Brain Connectivity Networks for IQ Prediction](https://mlsanity.com/paper/1810.12954/) (2018)
- [ManiFeSt: Manifold-based Feature Selection for Small Data Sets](https://mlsanity.com/paper/2207.08574/) (2022)

## Built on by (most cited first, 4 of 4)

- [Contextual Feature Selection with Conditional Stochastic Gates](https://mlsanity.com/paper/2312.14254/) (2023)
- [Multi-modal Differentiable Unsupervised Feature Selection](https://mlsanity.com/paper/2303.09381/) (2023)
- [Spectral Self-supervised Feature Selection](https://mlsanity.com/paper/2407.09061/) (2024)
- [Representational Difference Explanations](https://mlsanity.com/paper/2505.23917/) (2025)

## Abstract (arXiv)

Selecting subsets of features that differentiate between two conditions is a key task in a broad range of scientific domains. In many applications, the features of interest form clusters with similar effects on the data at hand. To recover such clusters we develop DiSC, a data-driven approach for detecting groups of features that differentiate between conditions. For each condition, we construct a graph whose nodes correspond to the features and whose weights are functions of the similarity between them for that condition. We then apply a spectral approach to compute subsets of nodes whose connectivity differs significantly between the condition-specific feature graphs. On the theoretical front, we analyze our approach with a toy example based on the stochastic block model. We evaluate DiSC on a variety of datasets, including MNIST, hyperspectral imaging, simulated scRNA-seq and task fMRI, and demonstrate that DiSC uncovers features that better differentiate between conditions compared to competing methods.