177 search results - page 5 / 36 » K-means clustering via principal component analysis |
NIPS
2008
14 years 11 months ago
2008
Kernel Principal Component Analysis (KPCA) is a popular generalization of linear PCA that allows non-linear feature extraction. In KPCA, data in the input space is mapped to highe...
ICML
2006
IEEE
15 years 10 months ago
2006
IEEE
Principal component analysis (PCA) minimizes the sum of squared errors (L2-norm) and is sensitive to the presence of outliers. We propose a rotational invariant L1-norm PCA (R1-PC...
ISBI
2006
IEEE
2006
IEEE
Mixture principal component analysis for distribution volume parametric imaging in brain PET studies
15 years 10 months ago
In this paper, we present a mixture Principal Component Analysis (mPCA)-based approach for voxel level quantification of dynamic positron emission tomography (PET) data in brain s...
91
Voted
WACV
2008
IEEE
15 years 3 months ago
2008
IEEE
In recent years, many researchers are studying object categorization problem. It is reported that bag of keypoints approach which is based on local features without topological in...
84
Voted
APPT
2005
Springer
15 years 3 months ago
2005
Springer
Identifying the patterns of large data sets is a key requirement in data mining. A powerful technique for this purpose is the principal component analysis (PCA). PCA-based clusteri...