# Performance of Gaussian Mixture Model Classifiers on Embedded Feature Spaces

arXiv: https://arxiv.org/abs/2410.13421 · HTML: https://mlsanity.com/paper/2410.13421/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 18 references that are in the map.
- First posted on arXiv 17 Oct 2024.

## Builds on (most cited first, 11 of 11)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances](https://mlsanity.com/paper/1306.0895/) (2013)
- [ImageBind: One Embedding Space To Bind Them All](https://mlsanity.com/paper/2305.05665/) (2023)
- [GMMSeg: Gaussian Mixture based Generative Semantic Segmentation Models](https://mlsanity.com/paper/2210.02025/) (2022)
- [Deep Polynomial Neural Networks](https://mlsanity.com/paper/2006.13026/) (2020)
- [Harmonic Convolutional Networks based on Discrete Cosine Transform](https://mlsanity.com/paper/2001.06570/) (2020)
- [Gradient-based training of Gaussian Mixture Models for High-Dimensional Streaming Data](https://mlsanity.com/paper/1912.09379/) (2019)
- [Gaussian Mixture Convolution Networks](https://mlsanity.com/paper/2202.09153/) (2022)
- [A Discriminative Gaussian Mixture Model with Sparsity](https://mlsanity.com/paper/1911.06028/) (2019)

## Built on by (most cited first, 3 of 3)

- [GMM-Based Comprehensive Feature Extraction and Relative Distance Preservation For Few-Shot Cross-Modal Retrieval](https://mlsanity.com/paper/2505.13306/) (2025)
- [SemiOccam: A Robust Semi-Supervised Image Recognition Network Using Sparse Labels](https://mlsanity.com/paper/2506.03582/) (2025)
- [Learning Representations from 3D Gaussian Splats](https://mlsanity.com/paper/2605.29549/) (2026)

## Abstract (arXiv)

Data embeddings with CLIP and ImageBind provide powerful features for the analysis of multimedia and/or multimodal data. We assess their performance here for classification using a Gaussian Mixture models (GMMs) based layer as an alternative to the standard Softmax layer. GMMs based classifiers have recently been shown to have interesting performances as part of deep learning pipelines trained end-to-end. Our first contribution is to investigate GMM based classification performance taking advantage of the embedded spaces CLIP and ImageBind. Our second contribution is in proposing our own GMM based classifier with a lower parameters count than previously proposed. Our findings are, that in most cases, on these tested embedded spaces, one gaussian component in the GMMs is often enough for capturing each class, and we hypothesize that this may be due to the contrastive loss used for training these embedded spaces that naturally concentrates features together for each class. We also observed that ImageBind often provides better performance than CLIP for classification of image datasets even when these embedded spaces are compressed using PCA.