# C-SALT: Mining Class-Specific ALTerations in Boolean Matrix Factorization

arXiv: https://arxiv.org/abs/1906.09907 · HTML: https://mlsanity.com/paper/1906.09907/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2019.

## Built on by (most cited first, 6 of 6)

- [Label-Descriptive Patterns and Their Application to Characterizing Classification Errors](https://mlsanity.com/paper/2110.09599/) (2021)
- [Finding Interpretable Class-Specific Patterns through Efficient Neural Search](https://mlsanity.com/paper/2312.04311/) (2023)
- [DiSC: Differential Spectral Clustering of Features](https://mlsanity.com/paper/2211.05314/) (2022)
- [Efficiently Factorizing Boolean Matrices using Proximal Gradient Descent](https://mlsanity.com/paper/2307.07615/) (2023)
- [Federated Binary Matrix Factorization using Proximal Optimization](https://mlsanity.com/paper/2407.01776/) (2024)
- [Algorithms for Boolean Matrix Factorization using Integer Programming and Heuristics](https://mlsanity.com/paper/2512.03807/) (2025)

## Abstract (arXiv)

Given labeled data represented by a binary matrix, we consider the task to derive a Boolean matrix factorization which identifies commonalities and specifications among the classes. While existing works focus on rank-one factorizations which are either specific or common to the classes, we derive class-specific alterations from common factorizations as well. Therewith, we broaden the applicability of our new method to datasets whose class-dependencies have a more complex structure. On the basis of synthetic and real-world datasets, we show on the one hand that our method is able to filter structure which corresponds to our model assumption, and on the other hand that our model assumption is justified in real-world application. Our method is parameter-free.