CASIA OpenIR  > 数字内容技术与服务研究中心  > 听觉模型与认知计算
Selecting feature subset with sparsity and low redundancy for unsupervised learning
Han, Jiuqi; Sun, Zhengya; Hao, Hongwei
Source PublicationKnowledge-Based Systems
2015-06
Volume86Pages:210 - 223
KeywordUnsupervised Feature Selection Nonnegative Spectral Analysis Sparsity And Low Redundancy
DOIhttp://dx.doi.org/10.1016/j.knosys.2015.06.008
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/11660
Collection数字内容技术与服务研究中心_听觉模型与认知计算
Affiliation中国科学院自动化研究所
Recommended Citation
GB/T 7714
Han, Jiuqi,Sun, Zhengya,Hao, Hongwei. Selecting feature subset with sparsity and low redundancy for unsupervised learning[J]. Knowledge-Based Systems,2015,86:210 - 223.
APA Han, Jiuqi,Sun, Zhengya,&Hao, Hongwei.(2015).Selecting feature subset with sparsity and low redundancy for unsupervised learning.Knowledge-Based Systems,86,210 - 223.
MLA Han, Jiuqi,et al."Selecting feature subset with sparsity and low redundancy for unsupervised learning".Knowledge-Based Systems 86(2015):210 - 223.
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