Dana Lahat - publications and more

Journal:

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] [Code]
2. D. Lahat, C. Jutten, and H. Shapiro. Schur's lemma for coupled reducibility and coupled normality. SIAM Journal on Matrix Analysis and Applications (SIMAX), Vol. 40, No. 3, 2019, pp. 998-1021. [paper].
3. D. Lahat and C. Jutten. Joint independent subspace analysis: uniqueness and identifiability. IEEE Transactions on Signal Processing, Vol. 67, No. 3, February 2019, pp. 684-699. [paper].
4. D. Lahat and C. Jutten. Joint independent subspace analysis using second-order statistics. IEEE Transactions on Signal Processing, Vol. 64, No. 18, September 2016, pp. 4891-4904. [paper] [Code available upon request]
5. D. Lahat, T. Adalı and C. Jutten. Multimodal data fusion: an overview of methods, challenges and prospects. Proceedings of the IEEE, Vol. 103, No. 9, September 2015, pp. 1449-1477. [paper]
6. D. Lahat, J.-F. Cardoso, and H. Messer. Blind separation of multidimensional components via subspace decomposition: performance analysis. IEEE Transactions on Signal Processing, Vol. 62, No. 11, June 2014, pp. 2894-2905. [paper]
7. D. Lahat, J.-F. Cardoso, and H. Messer. Second-order multidimensional ICA: performance analysis. IEEE Transactions on Signal Processing, Vol. 60, No. 9, September 2012, pp. 4598-4610. [paper]

Technical Reports:

1. D. Lahat and C. Jutten. A generalization to Schur's lemma with an application to joint independent subspace analysis. Tech. Rep. hal-01247899, GIPSA-Lab, Grenoble, France, December 2015. [paper]

Conference (peer-reviewed paper in conference proceedings):

1. D. Lahat and C. Févotte. Positive semidefinite matrix factorization based on truncated Wirtinger flow. EUSIPCO, Amsterdam, The Netherlands, January 2021. Virtual format. [paper] [Code]
2. 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] [Code] .
3. D. Lahat and C. Jutten. A new link between joint blind source separation using second order statistics and the canonical polyadic decomposition. LVA/ICA, Guildford, UK, July 2018. [paper]
   • This paper presents a new type of connection between independent vector analysis and tensor decomposition with application to blind source separation.
4. D. Lahat and C. Jutten. Joint independent subspace analysis by coupled block decomposition: non-identifiable cases. ICASSP, Calgary, Canada, April 2018. [paper]
5. D. Lahat and C. Jutten. Joint analysis of multiple datasets by cross-cumulant tensor (block) diagonalization. SAM, Rio de Janeiro, Brazil, July 2016. [paper]
6. D. Lahat and C. Jutten. An alternative proof for the identifiability of independent vector analysis using second order statistics. ICASSP, Shanghai, China, March 2016. [paper]
7. D. Lahat and C. Jutten. Joint independent subspace analysis: a quasi-Newton algorithm. LVA/ICA, Liberec, Czech Republic, August 2015, pp. 111-118. [paper] [Code]
8. D. Lahat and C. Jutten. Joint blind source separation of multidimensional components: model and algorithm. EUSIPCO, Lisbon, Portugal, September 2014, pp. 1417-1421. [paper] [poster]
9. D. Lahat, T. Adalı and C. Jutten. Challenges in multimodal data fusion. EUSIPCO, Lisbon, Portugal, September 2014, pp. 101-105. [paper] [presentation]
10. D. Lahat, J.-F. Cardoso, and H. Messer. Identifiability of second-order multidimensional ICA. EUSIPCO, Bucharest, Romania, August 27-31, 2012. [paper]
11. D. Lahat, J.-F. Cardoso, and H. Messer. Joint block diagonalization algorithms for optimal separation of multidimensional components. LVA/ICA, Tel Aviv, Israel, March 2012. [paper]
12. D. Lahat, J.-F. Cardoso, M. Le Jeune and H. Messer. Multidimensional ICA and its performance analysis, applied to CMB observations. ICASSP, Prague, Czech Republic, May 2011. [paper] [video]
13. D. Lahat, J.-F. Cardoso and H. Messer. ICA of correlated sources mismodeled as uncorrelated: performance analysis. IEEE Workshop on Statistical Signal Processing (SSP), Cardiff, Wales, UK. 31 August–3 September 2009. [paper]
14. D. Lahat, J.-F. Cardoso and H. Messer. Optimal performance of second-order multidimensional ICA. International Conference on Independent Component Analysis and Signal Separation (ICA), Paraty, Brazil, March 2009. [paper]
15. D. Lahat and A. J. Weiss. Performance analysis of a blind HOS separation criterion. IEEE 23rd Convention of Electrical and Electronics Engineers in Israel, Herzliya, Israel, September 2004, pp. 396-399. [paper]

Talks in Conferences without proceedings:

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. SIAM Conference on Applied Linear Algebra (SIAM LA21), 2021. Virtual format. [abstract]
2. D. Lahat and C. Jutten. Tensor and Coupled Decompositions in Block Terms: Uniqueness and Irreducibility. Signal Processing with Adaptive Sparse Structured Representations (SPARS), Toulouse, France, July 2019. [poster (in supplementary material)] [extended abstract]
3. D. Lahat and C. Jutten. Decompositions in Sum of Low-rank Block Terms: Can Block Size be Considered as a Type of Diversity? SIAM Annual Meeting (SIAM-AN18), Portland, Oregon, USA, July 2018.
4. D. Lahat and C. Jutten. Tensor and coupled decompositions in block terms: uniqueness and irreducibility. TRICAP 2018, Angel Fire Resort, New Mexico, USA, June 2018. [presentation (in supplementary material)]
5. D. Lahat and C. Jutten. Understanding the uniqueness of decompositions in low-rank block terms using Schur's lemma on irreducible representations. SIAM Conference on Applied Linear Algebra (SIAM-ALA18), Hong Kong, May 2018.
6. D. Lahat and C. Jutten. On the uniqueness of coupled matrix block diagonalization in the joint analysis of multiple datasets. SIAM Conference on Applied Linear Algebra (SIAM LA15), Atlanta, Georgia, USA, October 2015.
7. D. Lahat and C. Jutten. Multi-set data analysis and simultaneous matrix block diagonalization: models and algorithms. SIAM Conference on Computational Science and Engineering (SIAM CSE15), Salt Lake City, Utah, USA, March 2015.
8. A. Taub, M. Mintz, A. Magal, D. Lahat, H. Messer, M. Marcus-Kalish, and Y. Shacham. Brain-machine hybrid for the rehabilitation of a discrete motor learning function. The Nano2Life annual meeting, January 2008, Champéry, Switzerland.
9. A. Taub, M. Mintz, D. Lahat, H. Messer, M. Oksman, M. Marcus-Kalish, and Y. Shacham. Brain-machine hybrid for the rehabilitation of a discrete motor learning function. The Center for Complexity Science meeting, Bar-Ilan University, Ramat-Gan, Israel, February 15th 2007. (2nd prize in the poster session at the Center for Complexity Science open day).

PhD thesis:

• D. Lahat. Second-Order Multidimensional Independent Component Analysis: Theory and Methods. Tel Aviv University, Tel Aviv, Israel, 2013.

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