Beyond Pham's algorithm for joint diagonalization
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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.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 28 Nov 2018.
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Citing papers per year: 2021: 4.
4 papers.
- 2021Shared Independent Component Analysis for Multi-Subject Neuroimagingcited by 6
- 2021Enhancing ensemble learning and transfer learning in multimodal data analysis by adaptive dimensionality reductioncited by 2
- 2021A graph representation based on fluid diffusion model for data analysis: theoretical aspects and enhanced community detectioncited by 1
- 2021Leveraging Joint-Diagonalization in Transform-Learning NMFcited by 0
- 2021Leveraging Joint-Diagonalization in Transform-Learning NMF10 Dec 2021
- 2021A graph representation based on fluid diffusion model for data analysis: theoretical aspects and enhanced community detection7 Dec 2021
- 2021Shared Independent Component Analysis for Multi-Subject Neuroimaging26 Oct 2021
- 2021Enhancing ensemble learning and transfer learning in multimodal data analysis by adaptive dimensionality reduction8 May 2021