NMF versus ICA for blind source separation-Reference-Cited by-同舟云学术

NMF versus ICA for blind source separation

Published:2014-11-26 Issue:1 Volume:11 Page:25-48
ISSN:1862-5347
Container-title:Advances in Data Analysis and Classification
language:en
Short-container-title:Adv Data Anal Classif

Author:

Mirzal Andri

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Computer Science Applications,Statistics and Probability

Link

http://link.springer.com/content/pdf/10.1007/s11634-014-0192-4.pdf

Reference76 articles.

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3. Badeau R (2010) Stability analysis of multiplicative update algorithms and application to nonnegative matrix factorization. IEEE Trans Neural Netw 21(12):1869–1881

4. Berry M, Brown M, Langville A, Pauca P, Plemmons RJ (2007) Algorithms and applications for approximate nonnegative matrix factorization. Comput Stat Data Anal 52(1):155–173

5. Bertin N et al (2009) A tempering approach for Itakura–Saito non-negative matrix factorization. With application to music transcription. In: Proceedings of IEEE international conference on acoustics, speech and signal processing, pp 1545–1548

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