메뉴 건너뛰기




Volumn 25, Issue 5, 2007, Pages 531-543

Locality preserving CCA with applications to data visualization and pose estimation

Author keywords

Canonical correlation analysis (CCA); Data visualization; Dimensionality reduction; Locality preservation; Pose estimation

Indexed keywords

DATA STRUCTURES; FEATURE EXTRACTION; NONLINEAR SYSTEMS; PATTERN RECOGNITION;

EID: 33847056269     PISSN: 02628856     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.imavis.2006.04.014     Document Type: Article
Times cited : (226)

References (45)
  • 1
    • 84995332802 scopus 로고    scopus 로고
    • M. Borga, Canonical correlation: a tutorial, at , 1999.
  • 2
    • 0000107975 scopus 로고
    • Relations between two sets of variates
    • Hotelling H. Relations between two sets of variates. Biometrika 28 (1936) 321-377
    • (1936) Biometrika , vol.28 , pp. 321-377
    • Hotelling, H.1
  • 3
    • 84995318058 scopus 로고    scopus 로고
    • Yacov Hel-Or, The canonical correlations of color images and their use for demosaicing, HP Labs Technical Report, HPL-2003-164(R.1), February 2004.
  • 4
    • 25144502565 scopus 로고    scopus 로고
    • Dimensionality reduction of image features using the canonical contextual correlation projection
    • Loog M., van Ginneken B., and Duin R.P.W. Dimensionality reduction of image features using the canonical contextual correlation projection. Pattern Recognition 38 (2005) 2409-2418
    • (2005) Pattern Recognition , vol.38 , pp. 2409-2418
    • Loog, M.1    van Ginneken, B.2    Duin, R.P.W.3
  • 5
    • 0036505017 scopus 로고    scopus 로고
    • Multiset Canonical correlations analysis and multispectral, truly multitemporal remote sensing data
    • Nielsen A.A. Multiset Canonical correlations analysis and multispectral, truly multitemporal remote sensing data. IEEE Transactions on Image Processing 11 (2002) 293-305
    • (2002) IEEE Transactions on Image Processing , vol.11 , pp. 293-305
    • Nielsen, A.A.1
  • 6
    • 84995341785 scopus 로고    scopus 로고
    • M. Borga, Learning Multidimensional signal processing, PhD thesis, Department of Electrical Engineering, Linköping University, Linköping, Sweden, 1998.
  • 7
    • 84995318057 scopus 로고    scopus 로고
    • O. Friman, M, Borga, P. Lundberg, H. Knutsson, Canonical correlation as a tool in functional MRI data analysis, SSAB 2001, in: Proceedings of the SSAB Symposium on Image Analysis, Norrköping, Sweden, March 2001.
  • 8
    • 10044285992 scopus 로고    scopus 로고
    • Canonical correlation analysis: an overview with application to learning methods
    • Hardoon D.R., Szedmak S., and Shawe-Taylor J. Canonical correlation analysis: an overview with application to learning methods. Neural Computation 16 (2004) 2639-2664
    • (2004) Neural Computation , vol.16 , pp. 2639-2664
    • Hardoon, D.R.1    Szedmak, S.2    Shawe-Taylor, J.3
  • 9
    • 84958962514 scopus 로고    scopus 로고
    • T.V. Gestel, J.A.K. Suykens, J. De Brabanter, B. De Moor, J. Vandewalle, Kernel canonical correlation analysis and least squares support vector machines, in: Proceedings of the International Conference on Artificial Neural Networks (ICANN 2001) (2001) 384-389.
  • 10
    • 33846921465 scopus 로고    scopus 로고
    • Yo Horikawa, Use of autocorrelation kernels in kernel canonical correlation analysis for texture classification, in: N.R. Pal et al. (Eds.), ICONIP 2004, LNCS 3316, Springer-Verlag, Berlin, Heidelberg, 2004, pp. 1235-1240.
  • 11
    • 25144439113 scopus 로고    scopus 로고
    • A new method of feature fusion and its application in image recognition
    • Sun Q.-S., Zeng S.-G., Liu Y., Heng P.-A., and Xia D.-S. A new method of feature fusion and its application in image recognition. Pattern Recognition 38 (2005) 2437-2448
    • (2005) Pattern Recognition , vol.38 , pp. 2437-2448
    • Sun, Q.-S.1    Zeng, S.-G.2    Liu, Y.3    Heng, P.-A.4    Xia, D.-S.5
  • 12
    • 0038648412 scopus 로고    scopus 로고
    • Appearance models based on kernel canonical correlation analysis
    • Melzer T., Reiter M., and Bischof H. Appearance models based on kernel canonical correlation analysis. Pattern Recognition 36 (2003) 1961-1971
    • (2003) Pattern Recognition , vol.36 , pp. 1961-1971
    • Melzer, T.1    Reiter, M.2    Bischof, H.3
  • 14
    • 84995345746 scopus 로고    scopus 로고
    • Blza Fortuna, Kernel canonical correlation analysis with applications, in: SIKDD 2004 at Multiconference IS 2004, 12-15 October 2004, Ljubljana, Slovenia.
  • 16
  • 17
    • 0033708546 scopus 로고    scopus 로고
    • N. Vlassis, Y. Motomura, B. Krose, Supervised linear feature extraction for mobile robot localization, in: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA'00), San Francisco, CA, 2000, pp. 2979-2984.
  • 19
    • 4444314854 scopus 로고    scopus 로고
    • Extraction of correlated gene clusters from multiple genomic data by generalized kernel canonical correlation analysis
    • Yamanishi Y., Vert J.P., Nakaya A., and Kanehisa M. Extraction of correlated gene clusters from multiple genomic data by generalized kernel canonical correlation analysis. Bioinformatics 19 (2003) i323-i330
    • (2003) Bioinformatics , vol.19
    • Yamanishi, Y.1    Vert, J.P.2    Nakaya, A.3    Kanehisa, M.4
  • 20
    • 84995345759 scopus 로고    scopus 로고
    • Mathworks Inc., Matlab 7.0 Release 14 help: Statistics toolbox, January 2005.
  • 22
    • 84995307204 scopus 로고    scopus 로고
    • P.L. Lai, Neural implementations of canonical correlation analysis, PhD thesis, Dept. of computing and information systems, University of Paisley, Scotland, March 2000.
  • 23
    • 1242338180 scopus 로고    scopus 로고
    • A canonical correlation neural network for multicollinearity and functional data
    • Gou Z., and Fyfe C. A canonical correlation neural network for multicollinearity and functional data. Neural Networks 17 (2004) 285-293
    • (2004) Neural Networks , vol.17 , pp. 285-293
    • Gou, Z.1    Fyfe, C.2
  • 24
    • 0034704229 scopus 로고    scopus 로고
    • A global geometric framework for nonlinear c limensionality reduction
    • Tenenbaum J.B., Silva V.d., and Langford J.C. A global geometric framework for nonlinear c limensionality reduction. Science 290 (2000) 2319-2323
    • (2000) Science , vol.290 , pp. 2319-2323
    • Tenenbaum, J.B.1    Silva, V.d.2    Langford, J.C.3
  • 25
    • 0034704222 scopus 로고    scopus 로고
    • Nonlinear dimensionality reduction by locally linear embedding
    • Roweis S.T., and Saul L.K. Nonlinear dimensionality reduction by locally linear embedding. Science 290 (2000) 2323-2326
    • (2000) Science , vol.290 , pp. 2323-2326
    • Roweis, S.T.1    Saul, L.K.2
  • 28
    • 1542286524 scopus 로고    scopus 로고
    • Locality pursuit embedding
    • Min W., Lu K., and He X. Locality pursuit embedding. Pattern Recognition 37 (2004) 781-788
    • (2004) Pattern Recognition , vol.37 , pp. 781-788
    • Min, W.1    Lu, K.2    He, X.3
  • 29
    • 0348139702 scopus 로고    scopus 로고
    • Dimension reduction by local principal component analysis
    • Kambhatla N., and Leen T.K. Dimension reduction by local principal component analysis. Neural Computation 9 (1997) 1493-1516
    • (1997) Neural Computation , vol.9 , pp. 1493-1516
    • Kambhatla, N.1    Leen, T.K.2
  • 31
    • 15044364586 scopus 로고    scopus 로고
    • Locally linear discriminant analysis for multimodally distributed classes for face recognition with a single model image
    • Kim T.-K., and Kittler J. Locally linear discriminant analysis for multimodally distributed classes for face recognition with a single model image. IEEE Transactions on Pattern Analysis and Machine Intelligence 27 (2005) 318-327
    • (2005) IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.27 , pp. 318-327
    • Kim, T.-K.1    Kittler, J.2
  • 32
    • 22844442782 scopus 로고    scopus 로고
    • Automatic model selection for the optimization of SVM kernels
    • Ayat N.E., Cheriet M., and Suen C.Y. Automatic model selection for the optimization of SVM kernels. Pattern Recognition 38 (2005) 1733-1745
    • (2005) Pattern Recognition , vol.38 , pp. 1733-1745
    • Ayat, N.E.1    Cheriet, M.2    Suen, C.Y.3
  • 35
    • 0033337021 scopus 로고    scopus 로고
    • Fisher discriminant analysis with kernels
    • Hu Y.-H., Larsen J., Wilson E., and Douglas S. (Eds), IEEE Press, New York
    • Mika S., Ratsch G., Weston J., Scholköpf B., and Müller K.-R. Fisher discriminant analysis with kernels. In: Hu Y.-H., Larsen J., Wilson E., and Douglas S. (Eds). Neural Networks for Signal Processing IX (1999), IEEE Press, New York 41-48
    • (1999) Neural Networks for Signal Processing IX , pp. 41-48
    • Mika, S.1    Ratsch, G.2    Weston, J.3    Scholköpf, B.4    Müller, K.-R.5
  • 36
    • 2342517502 scopus 로고    scopus 로고
    • Think globally, fit locally: unsupervised learning of low dimensional manifolds
    • Saul L.K., and Roweis S.T. Think globally, fit locally: unsupervised learning of low dimensional manifolds. Journal of Machine Learning Research 4 (2003) 119-155
    • (2003) Journal of Machine Learning Research , vol.4 , pp. 119-155
    • Saul, L.K.1    Roweis, S.T.2
  • 39
    • 0034704189 scopus 로고    scopus 로고
    • The manifold ways of perception
    • Seung H.S., and Lee D. The manifold ways of perception. Science 290 (2000) 2268-2269
    • (2000) Science , vol.290 , pp. 2268-2269
    • Seung, H.S.1    Lee, D.2
  • 40
  • 42
    • 10044221725 scopus 로고    scopus 로고
    • B. Raytchev, I. Yoda, K. Sakaue, Head pose estimation by nonlinear manifold learning, in: IEEE Proceedings of the 17th International Conference on Pattern Recognition, 2004.
  • 44
    • 84995311405 scopus 로고    scopus 로고
    • S.A. Nene, S.K. Nayar, H. Murase, Columbia Object Image Library (COIL-20), Technical Report CUCS-005-96, February 1996.
  • 45
    • 0347243182 scopus 로고    scopus 로고
    • Nonlinear component analysis as a kernel eigenvalue problem
    • Schölkopf B., Smola A., and Müller K.-R. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation 10 (1998) 1299-1319
    • (1998) Neural Computation , vol.10 , pp. 1299-1319
    • Schölkopf, B.1    Smola, A.2    Müller, K.-R.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.