摘要
在传统距离度量的基础上,提出利用有效距离进行特征选择,并用于多模态分类.为了更好地反映样本间全局和局部关系,提出基于有效距离的多模态特征选择方法.该方法针对样本间全局关系进行建模,实现基于有效距离的特征选择,从而增强所选特征的判别性.在ADNI、UCI数据集上进行的分类实验表明,与传统方法相比,文中方法能有效提高多模态数据的分类性能.
Based on the traditional distance measurements, effective distance is adopted to implement feature selection for muhi-modality classification. To better reflect the global and local relationships among samples, an effective distance based multi-modality feature selection method is proposed. This method focuses on the global relationship among samples to build model, and effective distance based feature selection learning is realized. Thus, discriminative features are selected. To evaluate the efficiency of the proposed method, experiments are performed on the Alzheimer＇s disease neuroimaging initiative database and the UCI benchmark database. The experimental results demonstrate that compared with traditional feature selection methods using the Euclidean distance, the proposed method significantly improves the results of multi-modality classification.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2016年第7期658-664,共7页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金项目(No.61422204
61473149)
江苏省杰出青年自然科学基金项目(No.BK20130034)
南京航空航天大学基础研究基金项目(No.NE2013105)
南京航空航天大学研究生创新实验室开放基金项目(No.kfjj20151605)资助
高等院校博士学科点专项研究基金项目(No.20123218110009)~~
关键词
有效距离
特征选择
分类
多模态
Effective Distance, Feature Selection, Classification, Muhi-modality
