Code for Positive Semidefinite Matrix Factorization (PSDMF)

Matlab code is available here (click this link)

This Matlab code provides implementations of the PSDMF algorithms described in the following papers. The key idea is that each subproblem is updated based on a phase retrieval or affine rank minimization algorithm.

  1. D. Lahat, Y. Lang, V. Y. F. Tan, and C. Févotte. Positive Semidefinite Matrix Factorization: A Connection with Phase Retrieval and Affine Rank Minimization. IEEE Transactions on Signal Processing, Vol. 69, 2021, pp. 3059--3074. [preprint] [paper]
  2. D. Lahat and C. Févotte. Positive semidefinite matrix factorization based on truncated Wirtinger flow. EUSIPCO, Amsterdam, The Netherlands, January 2021. Virtual format. [paper]
  3. D. Lahat and C. Févotte. Positive semidefinite matrix factorization: a link to phase retrieval and a block gradient algorithm. ICASSP, Barcelona, Spain, May 2020. Virtual format. [paper].

Additional PSDMF algorithms and links to related code can be found in:

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