429 search results - page 7 / 86 » Unsupervised feature selection for principal components anal... |
SSPR
2004
Springer
14 years 27 days ago
2004
Springer
We present a tutorial survey on some recent approaches to unsupervised machine learning in the context of statistical pattern recognition. In statistical PR, there are two classica...
ENGL
2007
13 years 7 months ago
2007
— The paper aims at designing a scheme for automatic identification of a species from its genome sequence. A set of 64 three-tuple keywords is first generated using the four type...
NIPS
2008
13 years 9 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...
ISNN
2004
Springer
14 years 27 days ago
2004
Springer
Abstract. Principal Component Analysis (PCA) is a feature extraction approach directly based on a whole vector pattern and acquires a set of projections that can realize the best r...
KDD
2006
ACM
14 years 8 months ago
2006
ACM
Principal component analysis (PCA) has been extensively applied in data mining, pattern recognition and information retrieval for unsupervised dimensionality reduction. When label...