921 search results - page 36 / 185 » Kernel Principal Component Analysis |
IJON
2007
13 years 7 months ago
2007
Kernel principal component analysis (KPCA) extracts features of samples with an efficiency in inverse proportion to the size of the training sample set. In this paper, we develop...
ICRA
1998
IEEE
13 years 12 months ago
1998
IEEE
1 sensors is to construct a structural description from sensor data and to match this description to a previously acquired model [Crowley 85]. An alternative is to project individu...
CVPR
2006
IEEE
14 years 9 months ago
2006
IEEE
Linear Discriminant Analysis (LDA) is a well-known and important tool in pattern recognition with potential applications in many areas of research. The most famous and used formul...
ICML
2004
IEEE
14 years 8 months ago
2004
IEEE
Many clustering algorithms fail when dealing with high dimensional data. Principal component analysis (PCA) is a popular dimensionality reduction algorithm. However, it assumes a ...
IJCNN
2007
IEEE
14 years 1 months ago
2007
IEEE
— To approximate complex data, we propose new type of low-dimensional “principal object”: principal cubic complex. This complex is a generalization of linear and nonlinear pr...