We present a framework to extract the most important features (tree fragments) from a Tree Kernel (TK) space according to their importance in the target kernelbased machine, e.g. ...
Fisher score and Laplacian score are two popular feature selection algorithms, both of which belong to the general graph-based feature selection framework. In this framework, a fe...
Feiping Nie, Shiming Xiang, Yangqing Jia, Changshu...
We consider the problem of semi-supervised feature selection, where we are given a small amount of labeled examples and a large amount of unlabeled examples. Since a small number ...
We introduce a new framework for the automatic selection of the best views of 3D models. The approach is based on the assumption that models belonging to the same class of shapes ...
Feature selection methods have been successfully applied to text categorization but seldom applied to text clustering due to the unavailability of class label information. In this...