According to smoothness assumption,local topological structure can be shared between feature and label manifolds.This study proposes a new algorithm based on Local Tangent Space Alignment(LTSA)to implement the label e...According to smoothness assumption,local topological structure can be shared between feature and label manifolds.This study proposes a new algorithm based on Local Tangent Space Alignment(LTSA)to implement the label enhancement process.In general,we first establish a learning model for feature extraction in label space and use a feature extraction method of LTSA to guide the reconstruction of label manifolds.Then,we establish an unconstrained optimization model based on the optimal theory presented in this paper.The model is suitable for solving problems with a large number of sample points.Finally,the experiment results show that the algorithm can effectively improve the training speed and multilabel dataset prediction accuracy.展开更多
Multi-label learning is more complicated than single-label learning since the semantics of the instances are usually overlapped and not identical.The effectiveness of many algorithms often fails when the correlations ...Multi-label learning is more complicated than single-label learning since the semantics of the instances are usually overlapped and not identical.The effectiveness of many algorithms often fails when the correlations in the feature and label space are not fully exploited.To this end,we propose a novel non-negative matrix factorization(NMF)based modeling and training algorithm that learns from both the adjacencies of the instances and the labels of the training set.In the modeling process,a set of generators are constructed,and the associations among generators,instances,and labels are set up,with which the label prediction is conducted.In the training process,the parameters involved in the process of modeling are determined.Specifically,an NMF based algorithm is proposed to determine the associations between generators and instances,and a non-negative least square optimization algorithm is applied to determine the associations between generators and labels.The proposed algorithm fully takes the advantage of smoothness assumption,so that the labels are properly propagated.The experiments were carried out on six set of benchmarks.The results demonstrate the effectiveness of the proposed algorithms.展开更多
基金supported by the National Natural Science Foundation of China(Nos.61702270,41971343,and 61702271)China Postdoctoral Science Foundation(No.2017M621592)China Scholarship Council(No.CSC201906865006)。
文摘According to smoothness assumption,local topological structure can be shared between feature and label manifolds.This study proposes a new algorithm based on Local Tangent Space Alignment(LTSA)to implement the label enhancement process.In general,we first establish a learning model for feature extraction in label space and use a feature extraction method of LTSA to guide the reconstruction of label manifolds.Then,we establish an unconstrained optimization model based on the optimal theory presented in this paper.The model is suitable for solving problems with a large number of sample points.Finally,the experiment results show that the algorithm can effectively improve the training speed and multilabel dataset prediction accuracy.
基金support of the National Natural Science Foundation of China(Grant Nos.61402076,61572104,61103146)the Fundamental Research Funds for the Central Universities(DUT17JC04)the Project of the Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education,Jilin University(93K172017K03).
文摘Multi-label learning is more complicated than single-label learning since the semantics of the instances are usually overlapped and not identical.The effectiveness of many algorithms often fails when the correlations in the feature and label space are not fully exploited.To this end,we propose a novel non-negative matrix factorization(NMF)based modeling and training algorithm that learns from both the adjacencies of the instances and the labels of the training set.In the modeling process,a set of generators are constructed,and the associations among generators,instances,and labels are set up,with which the label prediction is conducted.In the training process,the parameters involved in the process of modeling are determined.Specifically,an NMF based algorithm is proposed to determine the associations between generators and instances,and a non-negative least square optimization algorithm is applied to determine the associations between generators and labels.The proposed algorithm fully takes the advantage of smoothness assumption,so that the labels are properly propagated.The experiments were carried out on six set of benchmarks.The results demonstrate the effectiveness of the proposed algorithms.
