A Taxonomy of Emerging Multilinear Discriminant Analysis Solutions in .NET

Implementation qrcode in .NET A Taxonomy of Emerging Multilinear Discriminant Analysis Solutions
Visual .net qrcode decoderwith .net
Using Barcode Control SDK for visual .net Control to generate, create, read, scan barcode image in visual .net applications.
A Taxonomy of Emerging Multilinear Discriminant Analysis Solutions
Quick Response Code barcode library for .net
use visual .net qr-codes printer tobuild qr bidimensional barcode with .net
QR Code JIS X 0510 scanner in .net
Using Barcode reader for .net vs 2010 Control to read, scan read, scan image in .net vs 2010 applications.
This chapter provides a comprehensive introduction to the area of multilinear learning algorithms, in particular the multilinear discriminant analysis (MLDA) algorithms, for the recognition of biometric signals, most of which are naturally tensor objects. Three typical projections are introduced rst: the vector-to-vector projections (VVP), the tensor-to-tensor projections (TTP) and the tensor-to-vector projections (TVP), and two general MLDA solutions are formulated: the MLDA-TTP and the MLDA-TVP. The choices of the separation criteria and the initialization methods are then presented and the relationships between LDA, MLDA-TTP, and MLDA-TVP are discussed. A taxonomy of MLDA variants is subsequently suggested; and it not only helps us to understand the existing mutlilinear algorithms, but also bene ts us in the development of new multilinear algorithms. Finally, the MLDA variants are experimentally evaluated on the CMU PIE database and the extended Yale database B to demonstrate their performance on the popular face recognition problem. The experimental results indicate that the MLDA solutions, and multilinear learning algorithms in general, are promising emerging areas for research and applications.
Barcode barcode library with .net
Using Barcode recognizer for .net vs 2010 Control to read, scan read, scan image in .net vs 2010 applications.
Incoporate bar code in .net
use visual .net barcode integration toencode barcode with .net
We would like to thank Cai Deng from the University of Illinois at Urbana-Champaign for making the standard-size face data available. We also would like to acknowledge all those who contributed to the research described in this chapter.
Encode qr on .net c#
using visual .net toadd qrcode for asp.net web,windows application
Compose qr bidimensional barcode for .net
use asp.net web pages denso qr bar code encoder todevelop denso qr bar code in .net
There are two types of decompositions used most in multilinear applications: the canonical decomposition (CANDECOMP) [21, 22, 53], which is also known as the parallel factors (PARAFAC) decomposition [21, 22, 54], and the TUCKER decomposition [21, 22, 55]. With the CANDECOMP decomposition, a tensor A can be decomposed into a linear combination of P rank-1 tensors:
Quick Response Code barcode library with visual basic
using barcode encoder for vs .net control to generate, create quick response code image in vs .net applications.
(1) (2) (N) p u p u p u p ,
Use upc-a for .net
using .net toassign universal product code version a on asp.net web,windows application
Deploy 1d barcode with .net
use .net linear barcode integration toadd linear barcode on .net
where P N In . With the TUCKER decomposition, a tensor A can be expressed n=1 as the product:
Barcode 128 barcode library with .net
use visual .net code 128 code set b printer toaccess code 128b on .net
P1 P2 PN
2/5 Standard printing for .net
use .net c 2 of 5 generator toprint c 2 of 5 on .net
p1 =1 p2 =1
pN =1 (1)
Matrix Barcode barcode library for .net
using barcode maker for sql server 2005 reporting services control to generate, create 2d matrix barcode image in sql server 2005 reporting services applications.
(1) (2) (N) S(p1 , p2 , . . . , pN )up1 up2 upN
Connect qr barcode with excel spreadsheets
use microsoft excel qr code encoder toembed qr-codes for microsoft excel
= S 1 U
2 U(2) N U(N) ,
Control barcode pdf417 data with vb
pdf417 data in visual basic.net
UCC-128 creator in vb.net
generate, create gs1128 none on visual basic.net projects
Control barcode 3/9 image in microsoft excel
using excel toincoporate code 39 full ascii in asp.net web,windows application
Linear Barcode integrating on .net
use aspx linear barcode integration todeploy linear 1d barcode on .net
where Pn In for n = 1, . . . , N, S = A 1 U(1) 2 U(2) . . . N U(N) , and (n) (n) (n) U(n) = u1 u2 . . . uPn is an In Pn matrix with orthonormal column vectors. The CONDECOMP decomposition is in fact a special case of the TUCKER decomposition.
1. L. D. Lathauwer, B. D. Moor, and J. Vandewalle, On the best rank-1 and rank-(R1 , R2 , . . . , RN ) approximation of higher-order tensors, SIAM J. Matrix Anal. Appl. 21(4):1324 1342, 2000. 2. S. Yan, D. Xu, Q. Yang, L. Zhang, X. Tang, and H. Zhang, Multilinear discriminant analysis for face recognition, IEEE Trans. Image Processing, 16(1):212 220, 2007. 3. H. Lu, J. Wang, and K. N. Plataniotis, A review on face and gait recognition: System, data and algorithms, in Advanced Signal Processing Handbook, 2nd edition, S. Stergiopoulos, editor, CRC Press, Boca Raton, FL, 2009, 000 000. 4. Y.-D. Kim and S. Choi, Color face tensor factorization and slicing for illumination-robust recognition, in Proceedings of the International Conference. on Biometrics, August 2007, pp. 19 28. 5. K. N. Plataniotis and A. N. Venetsanopoulos, Color Image Processing and Applications, SpringerVerlag, Berlin, 2000. 6. K. W. Bowyer, K. Chang, and P. Flynn, A survey of approaches and challenges in 3D and multi-modal 3D + 2D face recognition, Comput. Vis. Image Understanding 101(1):1 15, 2006. 7. S. Z. Li, C. Zhao, X. Zhu, and Z. Lei, 3D + 2D face recognition by fusion at both feature and decision levels, in Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures, October 2005. 8. C. Liu and H. Wechsler, Independent component analysis of gabor features for face recognition, IEEE Trans. Neural Networks 14(4):919 928, 2003. 9. R. Chellappa, A. Roy-Chowdhury, and S. Zhou, Recognition of Humans and Their Activities Using Video, Morgan & Claypool Publishers, San Rafael, CA, 2005. 10. H. Lu, K. N. Plataniotis, and A. N. Venetsanopoulos, A layered deformable model for gait analysis, in Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition, April 2006, pp. 249 254. 11. N. V. Boulgouris, D. Hatzinakos, and K. N. Plataniotis, Gait recognition: a challenging signal processing technology for biometrics, IEEE Signal Processing Mag. 22(6):000 000, 2005. 12. H. Lu, K. N. Plataniotis, and A. N. Venetsanopoulos, A full-body layered deformable model for automatic model-based gait recognition, EURASIP Journal on Advances in Signal Processing: Special Issue on Advanced Signal Processing and Pattern Recognition Methods for Biometrics, Vol. 2008, 2008, article ID 261317, 13 pages, doi:10.1155/2008/261317. 13. Z. Lei, R. Chu, R. He, S. Liao, and S. Z. Li, Face recognition by discriminant analysis with gabor tensor representation, in Proceedings of the. International Conference on Biometrics, August 2007, pp. 87 95. 14. H. Lu, K. N. Plataniotis, and A. N. Venetsanopoulos, MPCA: Multilinear principal component analysis of tensor objects, IEEE Trans. Neural Networks, 19(1):18 39, 2008. 15. G. Shakhnarovich and B. Moghaddam, Face recognition in subspaces, in S. Z. Li and A. K. Jain, editors, Handbook of Face Recognition, Springer-Verlag, Berlin, 2004, pp. 141 168. 16. J. Zhang, S. Z. Li, and J. Wang, Manifold learning and applications in recognition, in Y. P. Tan, K. H. Yap, and L. Wang, editors, Intelligent Multimedia Processing with Soft Computing, Springer-Verlag, Berlin, 2004, pp. 281 300. 17. M. H. C. Law and A. K. Jain, Incremental nonlinear dimensionality reduction by manifold learning, IEEE Trans. Pattern Anal. Mach. Intell. 28(3):377 391, 2006. 18. H. Lu, K. N. Plataniotis, and A. N. Venetsanopoulos, Gait recognition through MPCA plus LDA, in Proceedings of the Biometrics Symposium 2006, September 2006, pp. 1 6, doi:10.1109/ BCC.2006.4341613.