# A prototype-based model for set classification

arXiv: https://arxiv.org/abs/2408.13720 · HTML: https://mlsanity.com/paper/2408.13720/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 63 references that are in the map.
- First posted on arXiv 25 Aug 2024.

## Builds on (most cited first, 21 of 21)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods](https://mlsanity.com/paper/1911.02508/) (2019)
- [Model-Agnostic Interpretability of Machine Learning](https://mlsanity.com/paper/1606.05386/) (2016)
- [Neural Prototype Trees for Interpretable Fine-grained Image Recognition](https://mlsanity.com/paper/2012.02046/) (2020)
- [What do we need to build explainable AI systems for the medical domain?](https://mlsanity.com/paper/1712.09923/) (2017)
- [Interpretable Image Classification with Differentiable Prototypes Assignment](https://mlsanity.com/paper/2112.02902/) (2021)
- [Grassmannian Learning: Embedding Geometry Awareness in Shallow and Deep Learning](https://mlsanity.com/paper/1808.02229/) (2018)
- [Representing Sentences as Low-Rank Subspaces](https://mlsanity.com/paper/1704.05358/) (2017)
- [Document Similarity for Texts of Varying Lengths via Hidden Topics](https://mlsanity.com/paper/1903.10675/) (2019)
- [Visualisation and knowledge discovery from interpretable models](https://mlsanity.com/paper/2005.03632/) (2020)
- [Detection of extragalactic Ultra-Compact Dwarfs and Globular Clusters using Explainable AI techniques](https://mlsanity.com/paper/2201.01604/) (2022)
- [On Classification with Bags, Groups and Sets](https://mlsanity.com/paper/1406.0281/) (2014)
- [Interpretable Models Capable of Handling Systematic Missingness in Imbalanced Classes and Heterogeneous Datasets](https://mlsanity.com/paper/2206.02056/) (2022)
- [Generalized Relevance Learning Grassmann Quantization](https://mlsanity.com/paper/2403.09183/) (2024)

## Built on by (most cited first, 2 of 2)

- [Rethinking Graph-Based Document Classification: Learning Data-Driven Structures Beyond Heuristic Approaches](https://mlsanity.com/paper/2508.00864/) (2025)
- [An explainable approach to detect case law on housing and eviction issues within the HUDOC database](https://mlsanity.com/paper/2410.02978/) (2024)

## Abstract (arXiv)

Classification of sets of inputs (e.g., images and texts) is an active area of research within both computer vision (CV) and natural language processing (NLP). A common way to represent a set of vectors is to model them as linear subspaces. In this contribution, we present a prototype-based approach for learning on the manifold formed from such linear subspaces, the Grassmann manifold. Our proposed method learns a set of subspace prototypes capturing the representative characteristics of classes and a set of relevance factors automating the selection of the dimensionality of the subspaces. This leads to a transparent classifier model which presents the computed impact of each input vector on its decision. Through experiments on benchmark image and text datasets, we have demonstrated the efficiency of our proposed classifier, compared to the transformer-based models in terms of not only performance and explainability but also computational resource requirements.