48 search results - page 4 / 10 » Combining Variable Selection with Dimensionality Reduction |
AAAI
2012
12 years 5 days ago
2012
Probabilistic relational PCA (PRPCA) can learn a projection matrix to perform dimensionality reduction for relational data. However, the results learned by PRPCA lack interpretabi...
IWANN
2009
Springer
14 years 4 months ago
2009
Springer
Frequently, the number of input variables (features) involved in a problem becomes too large to be easily handled by conventional machine-learning models. This paper introduces a c...
BMCBI
2006
13 years 9 months ago
2006
Background: Evaluating the importance of the different sources of variations is essential in microarray data experiments. Complex experimental designs generally include various fa...
MLDM
2009
Springer
14 years 4 months 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 ...
BMCBI
2010
13 years 10 months ago
2010
Background: Generally speaking, different classifiers tend to work well for certain types of data and conversely, it is usually not known a priori which algorithm will be optimal ...