nach oben

Neural Computing and Applications

Erschienen in:

03.01.2021 | Original Article

Blind source separation for the analysis sparse model

verfasst von: Shuang Ma, Hongjuan Zhang, Zhuoyun Miao

Erschienen in: Neural Computing and Applications | Ausgabe 14/2021

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Sparsity of the signal has been shown to be very useful for blind source separation (BSS) problem which aims at recovering unknown sources from their mixtures. In this paper, we propose a novel algorithm based on the analysis sparse constraint of the source over an adaptive analysis dictionary to address BSS problem. This method has an alternating scheme by keeping all but one unknown fixed at a time so that the dictionary, the source, and the mixing matrix are estimated alternatively. In order to make better use of the sparsity constrain, \(l_{0}\)-norm is utilized directly for a more exact solution instead of its other relaxation, such as \(l_{\mathrm{p}}\)-norm (\(0<p\le 1\)). Numerical experiments show that the proposed method indeed improves the separation performance.

Vorheriger Artikel Integration of support vector regression and grey wolf optimization for estimating the ultimate bearing capacity in concrete-filled steel tube columns

Nächster Artikel An iterative gradient convolutional neural network and its application in endoscopic photoacoustic image formation from incomplete acoustic measurement

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Available at: http://md.cosmostat.org/Generalized_MCA.html.

Available at: http://md.cosmostat.org.

Available at: https://elad.cs.technion.ac.il/software/.

Hyvärinen A, Oja E (2000) Independent component analysis: algorithms and applications. Neural Netw 13(4):411–430CrossRef

Bell A, Sejnowski T (1995) An information-maximization approach to blind separation and blind deconvolution. Neural Comput 7(6):1129–1159CrossRef

Gaeta M, Lacoume JL (1990) Source separation without prior knowledge: the maximum likelihood solution. In: Proceeding EUSIPCO’90, pp 621–624

Belouchrani A, Cardoso JF (1994) Maximum likelihood source separation for discrete sources. In: Proceeding EUSIPCO’94, pp 768–771

Montoya-Martínez J, Cardoso JF, Gramfort A (2017) Caveats with stochastic gradient and maximum likelihood based ica for eeg. Lecture Note Computer Sci 10169:279–289CrossRef

Tillet P, Kung HT, Cox D (2017) Infomax-ica using hessian-free optimization. Proceeding ICASSP’2017, pp 2537–2541

Zibulevsky M (2003) Sparse source separation with relative newton method. Proceeding ICA 2003:897–902

Choi H, Choi S (2007) A relative trust-region algorithm for independent component analysis. Neurocomputing 70(7):1502–1510CrossRef

Byrd R, Lu P, Nocedal J, Zhu C (2003) A limited memory algorithm for bound constrained optimization. SIAM J Sci Comput 16(5):1190–1208MathSciNetCrossRef

10.

Ablin P, Cardoso J, Gramfort A (2018) Faster independent component analysis by preconditioning with hessian approximations. IEEE Trans Signal Process 66(15):4040–4049MathSciNetCrossRef

11.

Lee D, Seung H (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791CrossRef

12.

Boutsidis C, Gallopoulos E (2008) Svd based initialization: a head start for nonnegative matrix factorization. Pattern Recognit 41(4):1350–1362CrossRef

13.

Deng Z, Zhang S, Yang L, Zong M, Cheng D (2018) Sparse sample self-representation for subspace clustering. Neural Comput Appl 29:43–49CrossRef

14.

Zhu X, Suk HI, Huang H, Shen D (2017) Low-rank graph-regularized structured sparse regression for identifying genetic biomarkers. IEEE Trans Big Data 3(4):405–414CrossRef

15.

Zhang H, Wang S, Xu X, Chow TWS, Wu QMJ (2018) Tree2vector: learning a vectorial representation for tree-structured data. IEEE Trans Neural Netw Learn Syst 29(11):5304–5318MathSciNetCrossRef

16.

Alon U, Barkai N, Notterman D, Gish K, Ybarra S, Mack D, Levine A (1999) Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc Natl Acad Sci United States of America 96(12):6745–6750CrossRef

17.

Shipp MA et al (2002) Diffuse large b-cell lymphoma outcome prediction by gene-expressionprofilingandsupervisedmachinelearning. NatureMed 8(1):68–74

18.

Huang H, Zhao H, Li X, Ding S, Zhao L, Li Z (2018) An accurate and efficient device-free localization approach based on sparse coding in subspace. IEEE Access 6:61782–61799CrossRef

19.

Starck JL, Elad M, Donoho D (2004) Redundant multiscale transforms and their application for morphological component separation. Adv Imagin Electron Phys 132(4):287–348CrossRef

20.

Bobin J, Moudden Y, Starck JL, Elad M (2006) Morphological diversity and source separation. IEEE Signal Process Lett 13(7):409–412CrossRef

21.

Bobin J, Starck JL, Fadili J, Moudden Y (2007) Sparsity and morphological diversity in blind source separation. Image Process, IEEE Trans Image Process 16(11):2662–2674MathSciNetCrossRef

22.

Abolghasemi V, Ferdowsi S, Sanei S (2012) Blind separation of image sources via adaptive dictionary learning. IEEE Trans Image Process 21(6):2921–2930MathSciNetCrossRef

23.

Aharon M, Elad M, Bruckstein A (2006) K-svd: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans Signal Process 54(11):4311–4322CrossRef

24.

Zhao X, Zhou G, Dai W, Xu T, Wang W (2013) Joint image separation and dictionary learning. Proceedings DSP’2013, pp 1–6

25.

Dai W, Xu T, Wang W (2011) Simultaneous codeword optimization (simco) for dictionary update and learning. IEEE Trans Signal Process 60(12):6340–6353MathSciNetCrossRef

26.

Nam S, Davies M, Elad M, Gribonval R (2011) The cosparse analysis model and algorithms. Appl Comput Harmonic Anal 34(1):30–56MathSciNetCrossRef

27.

Giryes R, Nam S, Elad M, Gribonval R, Davies M (2012) Greedylike algorithms for the cosparse analysis model. Linear Algebra Appl 441(1):22–60MATH

28.

Elad M, Milanfar P, Rubinstein R (2007) Analysis versus synthesis in signal priors. Inverse Probl 23(3):947MathSciNetCrossRef

29.

Fang W, Wang H, Xu B, Zhang Y (2016) Blind source separation using analysis sparse constraint. Electron Lett 52(13):1112–1114CrossRef

30.

Rubinstein R, Peleg T, Elad M (2013) Analysis k-svd: a dictionary-learning algorithm for the analysis sparse model. IEEE Trans Signal Process 61(3):661–677MathSciNetCrossRef

31.

Zhang Y, Wang H, Yu T, Wang W (2013) Subset pursuit for analysis dictionary learning. Proceeding EUSIPCO’2013, pp 1–5

32.

Cai JF, Osher S, Shen Z (2009) Split bregman methods and frame based image restoration. Multiscale Model and Simul 8(2):337–369MathSciNetCrossRef

33.

Wu F, Jing X, Sun Y, Sun J, Huang L, Cui F, Sun Y (2018) Cross-project and within-project semisupervised software defect prediction: a unified approach. IEEE Trans Reliab 67(2):581–587CrossRef

34.

Wu F, Xi W, Han L, Jing X, Ji Y (2019) Multi-view synthesis and analysis dictionaries learning for classification. IEICE Trans Inf Syst E102 D(3):659–662CrossRef

Titel: Blind source separation for the analysis sparse model
verfasst von: Shuang Ma
Hongjuan Zhang
Zhuoyun Miao
Publikationsdatum: 03.01.2021
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 14/2021
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-020-05606-y

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Weitere Artikel der Ausgabe 14/2021

EEG extended source imaging with structured sparsity and -norm residual

An improved generative adversarial network for translating clothes from the human body to tiled image

Ingenious golden section search MPPT algorithm for PEM fuel cell power system

Neural network and fuzzy logic-based hybrid attitude controller designs of a fixed-wing UAV

Some new hybrid hesitant fuzzy weighted aggregation operators based on Archimedean and Dombi operations for multi-attribute decision making

Software defect prediction model based on LASSO–SVM

Premium Partner