Geometrical method using simplicial cones for overdetermined nonnegative blind source separation: Application to real PET images

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7191 LNCS, Issue , 2012, Pages 494-501

  Ouedraogo, Wendyam S B ^a,b,c   Souloumiac, Antoine ^a   Jaidane, Meriem ^b   Jutten, Christian ^c  

^a DIF   (France)

^b UNIVERSITY OF TUNIS EL MANAR   (Tunisia)

^c UNIV GRENOBLE ALPES   (France)

Author keywords

Blind Source Separation; Facet; Minimum Volume; Nonnegativity; Simplicial Cone

Indexed keywords

DATA SPACE; EMISSION TOMOGRAPHY; FACET; GEOMETRICAL METHODS; MINIMUM VOLUME; MIXED DATA; MIXING MATRIX; NON-NEGATIVITY; PET IMAGES; SCATTER PLOTS; SHRINKING ALGORITHMS; SYNTHETIC DATA; UNMIXING;

SEPARATION;

BLIND SOURCE SEPARATION;

EID: 84857265500     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-28551-6_61     Document Type: Conference Paper

References (9)

1. Puntonet, C., Mansour, A., Jutten, C.: A Geometrical Algorithm for Blind Source Separation. In: Gretsi 1995, Juan-les-Pins, pp. 273-276 (September 1995)
2. Babaie-Zadeh, M., Mansour, A., Jutten, C., Marvasti, F.: A Geometric Approach for Separating Several Speech Signals. In: Puntonet, C.G., Prieto, A.G. (eds.) ICA 2004. LNCS, vol. 3195, pp. 798-806. Springer, Heidelberg (2004) (Pubitemid 39751107)
3. Lazar, C., Nuzillard, D., Nowé, A.: A New Geometrical BSS Approach for Non Negative Sources. In: Vigneron, V., Zarzoso, V., Moreau, E., Gribonval, R., Vincent, E. (eds.) LVA/ICA 2010. LNCS, vol. 6365, pp. 530-537. Springer, Heidelberg (2010)
4. Chan, T.H., Ma, W.K., Chi, C.Y., Wang, Y.: A Convex Analysis Framework for Blind Separation of Non-Negative Sources. IEEE Transactions on Signal Processing 56, 5120-5134 (2008)
5. Li, J., Bioucas-Dias, J.M.: Minimum Volume Simplex Analysis: A Fast Algorithm to Unmix Hypersectral Data. In: IEEE International Symposium on Geoscience and Remote Sensing, vol. 3, pp. 250-253 (2008)
6. Chan, T.H., Chi, C.Y., Huang, Y.M., Ma, W.K.: A Convex Analysis-Based Minimum-Volume Enclosing Simplex Algorithm for Hyperspectral Unmixing. IEEE Transactions on Signal Processing 57, 4418-4432 (2009)
7. Craig, M.D.: Minimum-Volume Transforms for Remotely Sensed Data. IEEE Transactions on Geoscience and Remote Sensing 32, 542-552 (1994)
8. Henry, R.C.: History and fundamentals of multivariate air quality receptor models. Chemometrics and Intelligent Laboratory Systems 37, 37-42 (1997)
9. Ouedraogo, W.S.B., Souloumiac, A., Jaidane, M., Jutten, C.: Simplicial Cone Shrinking Algorithm for Unmixing Nonnegative Sources. In: Accepted to ICASSP 2012 (2012)