TY - GEN
T1 - Kernel Pooled Local Subspaces for Classification
AU - Zhang, Peng
AU - Peng, Jing
AU - Domeniconi, Carlotta
N1 - Publisher Copyright:
© 2003 IEEE.
PY - 2003
Y1 - 2003
N2 - We study the use of kernel subspace methods for learning low-dimensional representations for classification. We propose a kernel pooled local discriminant subspace method and compare it against several competing techniques: Principal Component Analysis (PCA), Kernel PCA (KPCA), and linear local pooling in classification problems. We evaluate the classification performance of the nearest-neighbor rule with each subspace representation. The experimental results demonstrate the effectiveness and performance superiority of the kernel pooled subspace method over competing methods such as PCA and KPCA in some classification problems.
AB - We study the use of kernel subspace methods for learning low-dimensional representations for classification. We propose a kernel pooled local discriminant subspace method and compare it against several competing techniques: Principal Component Analysis (PCA), Kernel PCA (KPCA), and linear local pooling in classification problems. We evaluate the classification performance of the nearest-neighbor rule with each subspace representation. The experimental results demonstrate the effectiveness and performance superiority of the kernel pooled subspace method over competing methods such as PCA and KPCA in some classification problems.
UR - http://www.scopus.com/inward/record.url?scp=84954451100&partnerID=8YFLogxK
U2 - 10.1109/CVPRW.2003.10060
DO - 10.1109/CVPRW.2003.10060
M3 - Conference contribution
AN - SCOPUS:84954451100
T3 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
BT - 2003 Conference on Computer Vision and Pattern Recognition Workshop, CVPRW 2003
PB - IEEE Computer Society
T2 - Conference on Computer Vision and Pattern Recognition Workshop, CVPRW 2003
Y2 - 16 June 2003 through 22 June 2003
ER -