# Beyond Pham's algorithm for joint diagonalization

arXiv: https://arxiv.org/abs/1811.11433 · HTML: https://mlsanity.com/paper/1811.11433/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2018.

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

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- [Enhancing ensemble learning and transfer learning in multimodal data analysis by adaptive dimensionality reduction](https://mlsanity.com/paper/2105.03682/) (2021)
- [A graph representation based on fluid diffusion model for data analysis: theoretical aspects and enhanced community detection](https://mlsanity.com/paper/2112.04388/) (2021)
- [Leveraging Joint-Diagonalization in Transform-Learning NMF](https://mlsanity.com/paper/2112.05664/) (2021)

## Abstract (arXiv)

The approximate joint diagonalization of a set of matrices consists in finding a basis in which these matrices are as diagonal as possible. This problem naturally appears in several statistical learning tasks such as blind signal separation. We consider the diagonalization criterion studied in a seminal paper by Pham (2001), and propose a new quasi-Newton method for its optimization. Through numerical experiments on simulated and real datasets, we show that the proposed method outper-forms Pham's algorithm. An open source Python package is released.