摘要
在模式识别和数据分析中,经常会遇到数据特征的高维问题。为了有效地进行数据分析,特征维数的削减或特征降维就显得异常重要。针对特征选择这一问题,依据概率密度距离准则,提出一个新的无监督特征排序方法。基于交叉验证的实验结果表明,该方法与现有的方法相比更为有效。
High dimensional datasets often exist in pattern recognition and data analysis, In order to effectively analyze these datasets, reducing their dimensional members is a pivotal step. Based on probability density interval, a novel unsupervised feature ranking approach is proposed, Several cross-validation experimental results demonstrate the advantage of our approach here over others.
出处
《计算机工程与设计》
CSCD
北大核心
2007年第19期4734-4737,共4页
Computer Engineering and Design
关键词
特征排序
特征选择
概率密度距离
Parzen窗口概率密度估计
降维
feature ranking
feature selection
probability density interval
Parzen probability density estimation
dimensionality reduction
