{"doi":"10.1007/978-3-540-30110-3_52","extern":"1","type":"conference","date_created":"2024-10-15T11:20:54Z","date_updated":"2024-12-02T14:30:47Z","month":"10","scopus_import":"1","page":"406–413","author":[{"full_name":"Bronstein, Alexander","first_name":"Alexander","orcid":"0000-0001-9699-8730","last_name":"Bronstein","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6"},{"last_name":"Bronstein","full_name":"Bronstein, Michael M.","first_name":"Michael M."},{"full_name":"Zibulevsky, Michael","first_name":"Michael","last_name":"Zibulevsky"}],"year":"2004","publication_identifier":{"issn":["0302-9743"],"eisbn":["9783540301103"],"eissn":["1611-3349"],"isbn":["9783540230564"]},"volume":3195,"date_published":"2004-10-27T00:00:00Z","alternative_title":["LNCS"],"oa_version":"None","title":"Blind source separation using the block-coordinate relative Newton method","user_id":"ea97e931-d5af-11eb-85d4-e6957dddbf17","publisher":"Springer Nature","language":[{"iso":"eng"}],"publication_status":"published","publication":"5th International Conference on Independent Component Analysis and Blind Signal Separation","conference":{"end_date":"2004-09-24","start_date":"2004-09-22","location":"Granada, Spain","name":"ICA: Independent Component Analysis and Blind Signal Separation"},"status":"public","_id":"18332","day":"27","citation":{"apa":"Bronstein, A. M., Bronstein, M. M., &#38; Zibulevsky, M. (2004). Blind source separation using the block-coordinate relative Newton method. In <i>5th International Conference on Independent Component Analysis and Blind Signal Separation</i> (Vol. 3195, pp. 406–413). Granada, Spain: Springer Nature. <a href=\"https://doi.org/10.1007/978-3-540-30110-3_52\">https://doi.org/10.1007/978-3-540-30110-3_52</a>","chicago":"Bronstein, Alex M., Michael M. Bronstein, and Michael Zibulevsky. “Blind Source Separation Using the Block-Coordinate Relative Newton Method.” In <i>5th International Conference on Independent Component Analysis and Blind Signal Separation</i>, 3195:406–413. Springer Nature, 2004. <a href=\"https://doi.org/10.1007/978-3-540-30110-3_52\">https://doi.org/10.1007/978-3-540-30110-3_52</a>.","mla":"Bronstein, Alex M., et al. “Blind Source Separation Using the Block-Coordinate Relative Newton Method.” <i>5th International Conference on Independent Component Analysis and Blind Signal Separation</i>, vol. 3195, Springer Nature, 2004, pp. 406–413, doi:<a href=\"https://doi.org/10.1007/978-3-540-30110-3_52\">10.1007/978-3-540-30110-3_52</a>.","ieee":"A. M. Bronstein, M. M. Bronstein, and M. Zibulevsky, “Blind source separation using the block-coordinate relative Newton method,” in <i>5th International Conference on Independent Component Analysis and Blind Signal Separation</i>, Granada, Spain, 2004, vol. 3195, pp. 406–413.","ama":"Bronstein AM, Bronstein MM, Zibulevsky M. Blind source separation using the block-coordinate relative Newton method. In: <i>5th International Conference on Independent Component Analysis and Blind Signal Separation</i>. Vol 3195. Springer Nature; 2004:406–413. doi:<a href=\"https://doi.org/10.1007/978-3-540-30110-3_52\">10.1007/978-3-540-30110-3_52</a>","short":"A.M. Bronstein, M.M. Bronstein, M. Zibulevsky, in:, 5th International Conference on Independent Component Analysis and Blind Signal Separation, Springer Nature, 2004, pp. 406–413.","ista":"Bronstein AM, Bronstein MM, Zibulevsky M. 2004. Blind source separation using the block-coordinate relative Newton method. 5th International Conference on Independent Component Analysis and Blind Signal Separation. ICA: Independent Component Analysis and Blind Signal Separation, LNCS, vol. 3195, 406–413."},"quality_controlled":"1","article_processing_charge":"No","intvolume":"      3195","abstract":[{"text":"Presented here is a generalization of the modified relative Newton method, recently proposed in [1] for quasi-maximum likelihood blind source separation. Special structure of the Hessian matrix allows to perform block-coordinate Newton descent, which significantly reduces the algorithm computational complexity and boosts its performance. Simulations based on artificial and real data show that the separation quality using the proposed algorithm outperforms other accepted blind source separation methods.","lang":"eng"}]}