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| 1 | 主分量稀疏化在GIS局部放电特征提取中的应用显示文摘特征提取是GIS局部放电模式识别的关键,通常情况下,由特征数据所构成的特征空间维数较高,不利于分类。基于此,本文引入主分量稀疏化思想,首先搭建了252k VGIS局部放电仿真实验平台,通过设置4种典型的GIS局部放电模型,采用超声波检测法获取了相应的信号。然后,通过主成分的贡献累积率自主确定稀疏度。结果表明,采用该方法可以实现特征量的有效提取,同时特征分量的聚类效果也得到了增强。 | 律方成 金虎 王子建 张波 | 2015 | 电工技术学报2015，30，8： | 8 |
| 2 | Variable Selection in Logistic Regression Model显示文摘Variable selection is one of the most important problems in pattern recognition. In linear regression model, there are many methods can solve this problem,such as Least absolute shrinkage and selection operator(LASSO) and many improved LASSO methods, but there are few variable selection methods in generalized linear models. We study the variable selection problem in logistic regression model. We propose a new variable selection method – the logistic elastic net, prove that it has grouping effect which means that the strongly correlated predictors tend to be in or out of the model together. The logistic elastic net is particularly useful when the number of predictors(p) is much bigger than the number of observations(n). By contrast, the LASSO is not a very satisfactory variable selection method in the case when p is more larger than n. The advantage and effectiveness of this method are demonstrated by real leukemia data and a simulation study. | ZHANG Shangli ZHANG Lili QIU Kuanmin LU Ying CAI Baigen | 2015 | Chinese Journal of Electronics2015，24，4： | 5 |
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