提出了一种基于非负稀疏表示(nonnegative sparse representation,NSR)的半监督学习标签传播算法.该算法首先构造一个稀疏概率图(sparse probability graph,SPG),其权重由非负稀疏表示算法计算的非负系数组成,自然地反映了各样本之间的...提出了一种基于非负稀疏表示(nonnegative sparse representation,NSR)的半监督学习标签传播算法.该算法首先构造一个稀疏概率图(sparse probability graph,SPG),其权重由非负稀疏表示算法计算的非负系数组成,自然地反映了各样本之间的聚类关系,避免了传统半监督学习算法中的邻居选择和参数设置过程;然后通过对未标记样本的标签进行迭代繁殖至收敛而获得所有样本的标签.在人脸识别、物体识别、UCI机器学习和TDT文本数据集上的实验结果表明采用非负稀疏表示的标签传播算法比典型的标签繁殖算法具有更好的分类准确率.展开更多
A novel framework is proposed to obtain physiologically meaningful features for Alzheimer's disease(AD)classification based on sparse functional connectivity and non-negative matrix factorization.Specifically,the ...A novel framework is proposed to obtain physiologically meaningful features for Alzheimer's disease(AD)classification based on sparse functional connectivity and non-negative matrix factorization.Specifically,the non-negative adaptive sparse representation(NASR)method is applied to compute the sparse functional connectivity among brain regions based on functional magnetic resonance imaging(fMRI)data for feature extraction.Afterwards,the sparse non-negative matrix factorization(sNMF)method is adopted for dimensionality reduction to obtain low-dimensional features with straightforward physical meaning.The experimental results show that the proposed framework outperforms the competing frameworks in terms of classification accuracy,sensitivity and specificity.Furthermore,three sub-networks,including the default mode network,the basal ganglia-thalamus-limbic network and the temporal-insular network,are found to have notable differences between the AD patients and the healthy subjects.The proposed framework can effectively identify AD patients and has potentials for extending the understanding of the pathological changes of AD.展开更多
文摘提出了一种基于非负稀疏表示(nonnegative sparse representation,NSR)的半监督学习标签传播算法.该算法首先构造一个稀疏概率图(sparse probability graph,SPG),其权重由非负稀疏表示算法计算的非负系数组成,自然地反映了各样本之间的聚类关系,避免了传统半监督学习算法中的邻居选择和参数设置过程;然后通过对未标记样本的标签进行迭代繁殖至收敛而获得所有样本的标签.在人脸识别、物体识别、UCI机器学习和TDT文本数据集上的实验结果表明采用非负稀疏表示的标签传播算法比典型的标签繁殖算法具有更好的分类准确率.
基金The Foundation of Hygiene and Health of Jiangsu Province(No.H2018042)the National Natural Science Foundation of China(No.61773114)the Key Research and Development Plan(Industry Foresight and Common Key Technology)of Jiangsu Province(No.BE2017007-3)
文摘A novel framework is proposed to obtain physiologically meaningful features for Alzheimer's disease(AD)classification based on sparse functional connectivity and non-negative matrix factorization.Specifically,the non-negative adaptive sparse representation(NASR)method is applied to compute the sparse functional connectivity among brain regions based on functional magnetic resonance imaging(fMRI)data for feature extraction.Afterwards,the sparse non-negative matrix factorization(sNMF)method is adopted for dimensionality reduction to obtain low-dimensional features with straightforward physical meaning.The experimental results show that the proposed framework outperforms the competing frameworks in terms of classification accuracy,sensitivity and specificity.Furthermore,three sub-networks,including the default mode network,the basal ganglia-thalamus-limbic network and the temporal-insular network,are found to have notable differences between the AD patients and the healthy subjects.The proposed framework can effectively identify AD patients and has potentials for extending the understanding of the pathological changes of AD.
