429 search results - page 15 / 86 » Unsupervised feature selection for principal components anal... |
KDD
2012
ACM
11 years 10 months ago
2012
ACM
The prevalent use of social media produces mountains of unlabeled, high-dimensional data. Feature selection has been shown effective in dealing with high-dimensional data for eï¬...
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...
ESANN
2006
13 years 9 months ago
2006
Random Forests were introduced by Breiman for feature (variable) selection and improved predictions for decision tree models. The resulting model is often superior to AdaBoost and ...
ISNN
2009
Springer
14 years 2 months ago
2009
Springer
Abstract. Nonlinear component analysis is a popular nonlinear feature extraction method. It generally uses eigen-decomposition technique to extract the principal components. But th...
WSCG
2004
13 years 9 months ago
2004
Principal Component Analysis (PCA) is a basis transformation to diagonalize an estimate of the covariance matrix of input data and, the new coordinates in the Eigenvector basis ar...