For supervised learning, feature selection algorithms attempt to maximise a given function of predictive accuracy. This function usually considers the ability of feature vectors t...
Many feature selection algorithms have been proposed in the past focusing on improving classification accuracy. In this work, we point out the importance of stable feature selecti...
Stability is an important yet under-addressed issue in feature selection from high-dimensional and small sample data. In this paper, we show that stability of feature selection ha...
Feature selection aims to reduce dimensionality for building comprehensible learning models with good generalization performance. Feature selection algorithms are largely studied ...
We address the problem of feature selection in a kernel space to select the most discriminative and informative features for classification and data analysis. This is a difficult ...
Bin Cao, Dou Shen, Jian-Tao Sun, Qiang Yang, Zheng...
In local feature?based face recognition systems, the topographical locations of feature extractors directly affect the discriminative power of a recognizer. Better recognition acc...