首页|Studies from Harbin Institute of Technology Shenzhen Have Provided New Data on C omputational Intelligence (Hyper-laplacian Regularized Concept Factorization In Low-rank Tensor Space for Multi-view Clustering)
Studies from Harbin Institute of Technology Shenzhen Have Provided New Data on C omputational Intelligence (Hyper-laplacian Regularized Concept Factorization In Low-rank Tensor Space for Multi-view Clustering)
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A new study on Machine Learning-Comp utational Intelligence is now available. According to news reporting from Shenzh en, People's Republic of China, by NewsRx journalists, research stated, "Tensor- oriented multi-view subspace clustering has achieved significant strides in asse ssing highorder correlations of multi-view data. Nevertheless, most of existing investigations are typically hampered by the two flaws: (1) Self-representation based tensor subspace learning usually induces high time and space complexity, and is limited in perceiving nonlinear local structure in the embedding space. ( 2) The tensor singular value decomposition model redistributes each singular val ue equally without considering the diverse importance among them." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), National Natural Science Foundation of Guangdong Province, N ational Natural Science Foundation of China Joint Fund Project Key Support Proje ct, Guangdong Provincial Key Laboratory of Novel Security Intelligence Technolog ies.
ShenzhenPeople's Republic of ChinaAsiaComputational IntelligenceMachine LearningHarbin Institute of Technology Shenzhen
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