Network embedding aims at learning low-dimensional representation of vertexes in a network and effectively preserving network structures.These representations can be used as features for many complex tasks on networks...Network embedding aims at learning low-dimensional representation of vertexes in a network and effectively preserving network structures.These representations can be used as features for many complex tasks on networks such as community detection and multi-label classification.Some classic methods based on the skip-gram model have been proposed to learn the representation of vertexes.However,these methods do not consider the global structure(i.e.,community structure)while sampling vertex sequences in network.To solve this problem,we suggest a novel sampling method which takes community information into consideration.It first samples dense vertex sequences by taking advantage of modularity function and then learns vertex representation by using the skip-gram model.Experimental results on the tasks of community detection and multi-label classification show that our method outperforms three state-of-the-art methods on learning the vertex representations in networks.展开更多
In order to reduce the effect of noises on DOA estimation,this paper proposes a direc-tion-of-arrival(DOA)estimation method using sparse representation with orthogonal projection(OPSR).The OPSR method obtains a new co...In order to reduce the effect of noises on DOA estimation,this paper proposes a direc-tion-of-arrival(DOA)estimation method using sparse representation with orthogonal projection(OPSR).The OPSR method obtains a new covariance matrix by projecting the covariance matrix of the array data to the signal subspace,leading to the elimination of the noise subspace.After-wards,based on the new covariance matrix after the orthogonal projection,a new sparse representa-tion model is established and employed for DOA estimation.Simulation results demonstrate that compared to other methods,the OPSR method has higher angle resolution and better DOA estima-tion performance in the cases of few snapshots and low SNRs.展开更多
There is a large amount of information in the network data that we canexploit. It is difficult for classical community detection algorithms to handle network data with sparse topology. Representation learning of netw...There is a large amount of information in the network data that we canexploit. It is difficult for classical community detection algorithms to handle network data with sparse topology. Representation learning of network data is usually paired with clustering algorithms to solve the community detection problem.Meanwhile, there is always an unpredictable distribution of class clusters outputby graph representation learning. Therefore, we propose an improved densitypeak clustering algorithm (ILDPC) for the community detection problem, whichimproves the local density mechanism in the original algorithm and can betteraccommodate class clusters of different shapes. And we study the communitydetection in network data. The algorithm is paired with the benchmark modelGraph sample and aggregate (GraphSAGE) to show the adaptability of ILDPCfor community detection. The plotted decision diagram shows that the ILDPCalgorithm is more discriminative in selecting density peak points compared tothe original algorithm. Finally, the performance of K-means and other clusteringalgorithms on this benchmark model is compared, and the algorithm is proved tobe more suitable for community detection in sparse networks with the benchmarkmodel on the evaluation criterion F1-score. The sensitivity of the parameters ofthe ILDPC algorithm to the low-dimensional vector set output by the benchmarkmodel GraphSAGE is also analyzed.展开更多
深度子空间聚类(DSC)基于原始数据位于低维非线性子空间的集合中的假设。其中深度子空间聚类多尺度表示学习方法在深度自编码器的基础上,将每一层的编码器与对应的解码器之间都添加全连接层,并以此捕获多尺度的特征,但它没有深度分析多...深度子空间聚类(DSC)基于原始数据位于低维非线性子空间的集合中的假设。其中深度子空间聚类多尺度表示学习方法在深度自编码器的基础上,将每一层的编码器与对应的解码器之间都添加全连接层,并以此捕获多尺度的特征,但它没有深度分析多尺度特征的性质,也没有考虑输入数据和输出数据之间多尺度的重构损失。为了解决上述问题,首先建立每个网络层的重构损失函数,监督不同级别编码器参数的学习;然后利用多尺度特征共有的自表示矩阵和特有的自表示矩阵的和具有块对角性,提出更有效的多尺度自表示模块;最后分析不同尺度特征特有的自表示矩阵之间的多样性,有效地利用了多尺度的特征矩阵。在此基础上,提出一种基于一致性和多样性的多尺度自表示学习的深度子空间聚类(MSCD-DSC)方法。在数据集Extended Yale B、ORL、COIL20和Umist上的实验结果表明,相较于次优的MLRDSC(Multi-Level Representation learning for Deep Subspace Clustering),MSCD-DSC的聚类错误率分别降低了15.44%、2.22%、3.37%和13.17%,表明MSCD-DSC的聚类效果优于已有的方法。展开更多
针对深度多视子空间聚类网络在进行数据融合时不能区分各视图可靠性,以及缺乏对多视数据间一致性信息与互补性信息的利用等问题,提出一种基于自适应的权重融合深度多视子空间聚类(deep multi-view subspace clustering based on adaptiv...针对深度多视子空间聚类网络在进行数据融合时不能区分各视图可靠性,以及缺乏对多视数据间一致性信息与互补性信息的利用等问题,提出一种基于自适应的权重融合深度多视子空间聚类(deep multi-view subspace clustering based on adaptive weight fusion,DMSC-AWF)方法。首先,通过使各视图共享同一个自表示层学习一个公共的表示矩阵,同时为各视图分别构建自表示层来学习各视图特定的表示矩阵,以此确保多视数据的一致性信息和互补性信息得以有效利用。然后,在共享自表示层基础上引入注意力模块来量化不同视图的重要性,注意力模块自适应地为每个视图数据分配权重。最后,在4个公开数据集上进行聚类实验,该方法的聚类结果相比于对比方法有明显的提升,并且,通过退化实验验证了注意力模块学习视权重的有效性和重要性。展开更多
基金Project supported by the National Natural Science Foundation of China(Grant No.61673085)the Program from the Sichuan Provincial Science and Technology,China(Grant No.2018RZ0081)the Fundamental Research Funds of China West Normal University(Grant No.17E063)。
文摘Network embedding aims at learning low-dimensional representation of vertexes in a network and effectively preserving network structures.These representations can be used as features for many complex tasks on networks such as community detection and multi-label classification.Some classic methods based on the skip-gram model have been proposed to learn the representation of vertexes.However,these methods do not consider the global structure(i.e.,community structure)while sampling vertex sequences in network.To solve this problem,we suggest a novel sampling method which takes community information into consideration.It first samples dense vertex sequences by taking advantage of modularity function and then learns vertex representation by using the skip-gram model.Experimental results on the tasks of community detection and multi-label classification show that our method outperforms three state-of-the-art methods on learning the vertex representations in networks.
基金the National Natural Science Foundation of China(No.61701133)the Fundamental Research Funds for the Central Universities(No.D5000210641).
文摘In order to reduce the effect of noises on DOA estimation,this paper proposes a direc-tion-of-arrival(DOA)estimation method using sparse representation with orthogonal projection(OPSR).The OPSR method obtains a new covariance matrix by projecting the covariance matrix of the array data to the signal subspace,leading to the elimination of the noise subspace.After-wards,based on the new covariance matrix after the orthogonal projection,a new sparse representa-tion model is established and employed for DOA estimation.Simulation results demonstrate that compared to other methods,the OPSR method has higher angle resolution and better DOA estima-tion performance in the cases of few snapshots and low SNRs.
基金The National Natural Science Foundation of China(No.61762031)The Science and Technology Major Project of Guangxi Province(NO.AA19046004)The Natural Science Foundation of Guangxi(No.2021JJA170130).
文摘There is a large amount of information in the network data that we canexploit. It is difficult for classical community detection algorithms to handle network data with sparse topology. Representation learning of network data is usually paired with clustering algorithms to solve the community detection problem.Meanwhile, there is always an unpredictable distribution of class clusters outputby graph representation learning. Therefore, we propose an improved densitypeak clustering algorithm (ILDPC) for the community detection problem, whichimproves the local density mechanism in the original algorithm and can betteraccommodate class clusters of different shapes. And we study the communitydetection in network data. The algorithm is paired with the benchmark modelGraph sample and aggregate (GraphSAGE) to show the adaptability of ILDPCfor community detection. The plotted decision diagram shows that the ILDPCalgorithm is more discriminative in selecting density peak points compared tothe original algorithm. Finally, the performance of K-means and other clusteringalgorithms on this benchmark model is compared, and the algorithm is proved tobe more suitable for community detection in sparse networks with the benchmarkmodel on the evaluation criterion F1-score. The sensitivity of the parameters ofthe ILDPC algorithm to the low-dimensional vector set output by the benchmarkmodel GraphSAGE is also analyzed.
文摘深度子空间聚类(DSC)基于原始数据位于低维非线性子空间的集合中的假设。其中深度子空间聚类多尺度表示学习方法在深度自编码器的基础上,将每一层的编码器与对应的解码器之间都添加全连接层,并以此捕获多尺度的特征,但它没有深度分析多尺度特征的性质,也没有考虑输入数据和输出数据之间多尺度的重构损失。为了解决上述问题,首先建立每个网络层的重构损失函数,监督不同级别编码器参数的学习;然后利用多尺度特征共有的自表示矩阵和特有的自表示矩阵的和具有块对角性,提出更有效的多尺度自表示模块;最后分析不同尺度特征特有的自表示矩阵之间的多样性,有效地利用了多尺度的特征矩阵。在此基础上,提出一种基于一致性和多样性的多尺度自表示学习的深度子空间聚类(MSCD-DSC)方法。在数据集Extended Yale B、ORL、COIL20和Umist上的实验结果表明,相较于次优的MLRDSC(Multi-Level Representation learning for Deep Subspace Clustering),MSCD-DSC的聚类错误率分别降低了15.44%、2.22%、3.37%和13.17%,表明MSCD-DSC的聚类效果优于已有的方法。
文摘修正极坐标表示(Modified Polar Representation,MPR)实现了近场定位与远场测向模型的统一表达,克服了定位或测向对目标距离先验信息的依赖,巧妙地规避了传统坐标系下因远场距离模糊及距离-角度相互耦合导致的定位精度下降.然而现有MPR定位方法存在性能和稳健性不足、边界条件不清晰等问题,无法满足工程应用的实际需求.本文从子空间的角度着手以求解MPR下的时差(Time Difference Of Arrival,TDOA)定位问题,将角度和逆距离的估计分离到两个正交的空间,从而提高算法性能和稳健性.首先利用零空间投影消除逆距离以求解角度的最优估计,然后再代回原方程求解逆距离.在求解逆距离估计时,考虑角度估计误差使得矩阵出现秩亏,可通过向矩阵非零特征值对应的子空间投影解决秩亏的问题,再由加权最小二乘直接求得逆距离的最优估计.与现有研究中最优闭式解广义信赖域子问题(Generalized Trust Region Sub-problem,GTRS)相比,分析和仿真实验都证实所提算法具有更好的性能和大噪声场景下的稳健性.本文还分析了包括所提算法在内的现有基于MPR的TDOA定位算法的局限性,明确了不同算法对最小传感器数量和适用场景的的要求,为工程中算法的选择提供参考.
文摘针对深度多视子空间聚类网络在进行数据融合时不能区分各视图可靠性,以及缺乏对多视数据间一致性信息与互补性信息的利用等问题,提出一种基于自适应的权重融合深度多视子空间聚类(deep multi-view subspace clustering based on adaptive weight fusion,DMSC-AWF)方法。首先,通过使各视图共享同一个自表示层学习一个公共的表示矩阵,同时为各视图分别构建自表示层来学习各视图特定的表示矩阵,以此确保多视数据的一致性信息和互补性信息得以有效利用。然后,在共享自表示层基础上引入注意力模块来量化不同视图的重要性,注意力模块自适应地为每个视图数据分配权重。最后,在4个公开数据集上进行聚类实验,该方法的聚类结果相比于对比方法有明显的提升,并且,通过退化实验验证了注意力模块学习视权重的有效性和重要性。
