31 search results - page 3 / 7 » The Effect of Principal Component Analysis on Machine Learni... |
ICML
2004
IEEE
14 years 7 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 ...
AIPR
2003
IEEE
13 years 11 months ago
2003
IEEE
Although hyperspectral images provide abundant information about objects, their high dimensionality also substantially increases computational burden. Dimensionality reduction off...
ICML
2003
IEEE
14 years 7 months ago
2003
IEEE
Feature selection, as a preprocessing step to machine learning, has been effective in reducing dimensionality, removing irrelevant data, increasing learning accuracy, and improvin...
MLDM
2009
Springer
14 years 29 days ago
2009
Springer
This paper describes recognition of emotions of an unkown person during natural walking. As gait data is redundant, high dimensional and variable, effective feature extraction is ...
PAMI
2010
13 years 4 months ago
2010
—This paper considers feature selection for data classification in the presence of a huge number of irrelevant features. We propose a new feature selection algorithm that addres...