TY - GEN
T1 - Fast statistical learning with Kernel-based simple-fda
AU - Nakaura, K.
AU - Karungaru, S.
AU - Akashi, T.
AU - Mitsukura, Y.
AU - Fukumi, M.
PY - 2008/12/1
Y1 - 2008/12/1
N2 - In this paper, new statistical learning algorithms with kernel function are presented. Recently, iterative learning algorithms for obtaining eigenvectors in the principal component analysis (PCA) have been presented in the field of pattern recognition and neural network. However, the Fisher linear discriminant analysis (FLDA) has been used in many fields, especially face image analysis. The drawback of FLDA is a long computational time based on a large-sized covariance matrix and the issue that the within-class covariance matrix is usually singular. In order to overcome this difficulty, we proposed the feature generation method Simple-FLDA which is approximately derived from geometrical interpretation of FLDA. This algorithm is similar to Simple-PCA and does not need matrix operation. In this paper, new statistical kernel based learning algorithms are presented. They are extended versions of Simple-PCA and Simple-FLDA to nonlinear space using the kernel function. Their preliminary simulation results are given for a simple face recognition problem.
AB - In this paper, new statistical learning algorithms with kernel function are presented. Recently, iterative learning algorithms for obtaining eigenvectors in the principal component analysis (PCA) have been presented in the field of pattern recognition and neural network. However, the Fisher linear discriminant analysis (FLDA) has been used in many fields, especially face image analysis. The drawback of FLDA is a long computational time based on a large-sized covariance matrix and the issue that the within-class covariance matrix is usually singular. In order to overcome this difficulty, we proposed the feature generation method Simple-FLDA which is approximately derived from geometrical interpretation of FLDA. This algorithm is similar to Simple-PCA and does not need matrix operation. In this paper, new statistical kernel based learning algorithms are presented. They are extended versions of Simple-PCA and Simple-FLDA to nonlinear space using the kernel function. Their preliminary simulation results are given for a simple face recognition problem.
UR - http://www.scopus.com/inward/record.url?scp=60349108293&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=60349108293&partnerID=8YFLogxK
U2 - 10.1109/SITIS.2008.52
DO - 10.1109/SITIS.2008.52
M3 - Conference contribution
AN - SCOPUS:60349108293
SN - 9780769534930
T3 - SITIS 2008 - Proceedings of the 4th International Conference on Signal Image Technology and Internet Based Systems
SP - 333
EP - 337
BT - SITIS 2008 - Proceedings of the 4th International Conference on Signal Image Technology and Internet Based Systems
T2 - 4th International Conference on Signal Image Technology and Internet Based Systems, SITIS 2008
Y2 - 30 November 2008 through 3 December 2008
ER -