TY - GEN
T1 - Smile stages classification based on Kernel Laplacian-lips using selection of non linear function maximum value
AU - Hery Pumomo, Mauridhi
AU - Arif Sarjono, Tri
AU - Muntasa, Arif
PY - 2010
Y1 - 2010
N2 - A common strategy for extracting the feature and to preserve the global structure such as Principal Component Analysis, Two Dimensional Principal Component Analysis and Linear Discriminant Analysis have been used. These schemes are a classical linear technique that projects the data along the directions of maximum variance. To improve the performance, Locality Preserving Projection is used. The objective is to preserve the intrinsic geometry of the data, and local structure. However, Locality Preserving Projection has the weakness, restrictiveness to separate the non linear data set. A novel approach to separate non Iinier data set based on selection of non Iinier function maximum value by using Kernel is proposed. Kernel maps the input to feature space by using three non linear functions; the result of mapping will be selected the maximum value. To avoid singularity, the result of the selected value will be processed by using Principal Component Analysis. Furthermore, Laplacian is used to process the result of Principal Component Analysis to achieve the local structure. The performance of the proposed method is tested to classify smile stages pattern. The experiment result shows that, the proposed method has higher classification rate than Two Dimensional Principal Component Analysis and combining of Principal Component Analysis, Linear Discriminant Analysis and Support Vector Machine.
AB - A common strategy for extracting the feature and to preserve the global structure such as Principal Component Analysis, Two Dimensional Principal Component Analysis and Linear Discriminant Analysis have been used. These schemes are a classical linear technique that projects the data along the directions of maximum variance. To improve the performance, Locality Preserving Projection is used. The objective is to preserve the intrinsic geometry of the data, and local structure. However, Locality Preserving Projection has the weakness, restrictiveness to separate the non linear data set. A novel approach to separate non Iinier data set based on selection of non Iinier function maximum value by using Kernel is proposed. Kernel maps the input to feature space by using three non linear functions; the result of mapping will be selected the maximum value. To avoid singularity, the result of the selected value will be processed by using Principal Component Analysis. Furthermore, Laplacian is used to process the result of Principal Component Analysis to achieve the local structure. The performance of the proposed method is tested to classify smile stages pattern. The experiment result shows that, the proposed method has higher classification rate than Two Dimensional Principal Component Analysis and combining of Principal Component Analysis, Linear Discriminant Analysis and Support Vector Machine.
KW - Classification
KW - Kernel and Laplacian
KW - Local s tructure
KW - Locality Preserving Projection
KW - Smile s tages
UR - http://www.scopus.com/inward/record.url?scp=78650156916&partnerID=8YFLogxK
U2 - 10.1109/VECIMS.2010.5609338
DO - 10.1109/VECIMS.2010.5609338
M3 - Conference contribution
AN - SCOPUS:78650156916
SN - 9781424459063
T3 - VECIMS 2010 - 2010 IEEE International Conference on Virtual Environments, Human-Computer Interfaces and Measurement Systems, Proceedings
SP - 151
EP - 156
BT - VECIMS 2010 - 2010 IEEE International Conference on Virtual Environments, Human-Computer Interfaces and Measurement Systems, Proceedings
T2 - 2010 8th IEEE International Conference on Virtual Environments, Human-Computer Interfaces, and Measurement Systems, VECIMS 2010
Y2 - 6 September 2010 through 8 September 2010
ER -