TY - JOUR
T1 - A nonlinear principal component analysis of image data
AU - Saegusa, Ryo
AU - Sakano, Hitoshi
AU - Hashimoto, Shuji
PY - 2005
Y1 - 2005
N2 - Principal Component Analysis (PCA) has been applied in various areas such as pattern recognition and data compression. In some cases, however, PCA does not extract the characteristics of the data-distribution efficiently. In order to overcome this problem, we have proposed a novel method of Nonlinear PCA which preserves the order of the principal components. In this paper, we reduce the dimensionality of image data using the proposed method, and examine its effectiveness in the compression and recognition of images.
AB - Principal Component Analysis (PCA) has been applied in various areas such as pattern recognition and data compression. In some cases, however, PCA does not extract the characteristics of the data-distribution efficiently. In order to overcome this problem, we have proposed a novel method of Nonlinear PCA which preserves the order of the principal components. In this paper, we reduce the dimensionality of image data using the proposed method, and examine its effectiveness in the compression and recognition of images.
KW - Dimensionality reduction
KW - Image
KW - Neural network
KW - Nonlinear PCA
UR - http://www.scopus.com/inward/record.url?scp=33645679063&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33645679063&partnerID=8YFLogxK
U2 - 10.1093/ietisy/e88-d.10.2242
DO - 10.1093/ietisy/e88-d.10.2242
M3 - Article
AN - SCOPUS:33645679063
SN - 0916-8532
VL - E88-D
SP - 2242
EP - 2248
JO - IEICE Transactions on Information and Systems
JF - IEICE Transactions on Information and Systems
IS - 10
ER -