# When compressive learning fails: blame the decoder or the sketch?

arXiv: https://arxiv.org/abs/2009.08273 · HTML: https://mlsanity.com/paper/2009.08273/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2020.

## Built on by (most cited first, 1 of 1)

- [Compressive Meta-Learning](https://mlsanity.com/paper/2508.11090/) (2025)

## Abstract (arXiv)

In compressive learning, a mixture model (a set of centroids or a Gaussian mixture) is learned from a sketch vector, that serves as a highly compressed representation of the dataset. This requires solving a non-convex optimization problem, hence in practice approximate heuristics (such as CLOMPR) are used. In this work we explore, by numerical simulations, properties of this non-convex optimization landscape and those heuristics.