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学者姓名：胡永利
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摘要 ：
This study proposes a novel multi-view soft block diagonal representation framework for clustering complete and incomplete multi-view data. First, given that the multi-view self-representation model offers better performance in exploring the intrinsic structure of multi-view data, it can be nicely adopted to individually construct a graph for each view. Second, since an ideal block diagonal graph is beneficial for clustering, a 'soft' block diagonal affinity matrix is constructed by fusing multiple previous graphs. The soft diagonal block regulariser encourages a matrix to approximately have (not exactly) K diagonal blocks, where K is the number of clusters. This strategy adds robustness to noise and outliers. Third, to handle incomplete multi-view data, multiple indicator matrices are utilised, which can mark the position of missing elements of each view. Finally, the alternative direction of multipliers algorithm is employed to optimise the proposed model, and the corresponding algorithm complexity and convergence are also analysed. Extensive experimental results on several real-world datasets achieve the best performance among the state-of-the-art complete and incomplete clustering methods, which proves the effectiveness of the proposed methods.
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| GB/T 7714 | Hu, Yongli , Luo, Cuicui , Wang, Boyue et al. Complete/incomplete multi-view subspace clustering via soft block-diagonal-induced regulariser [J]. | IET COMPUTER VISION , 2021 , 15 (8) : 618-632 . |
| MLA | Hu, Yongli et al. "Complete/incomplete multi-view subspace clustering via soft block-diagonal-induced regulariser" . | IET COMPUTER VISION 15 . 8 (2021) : 618-632 . |
| APA | Hu, Yongli , Luo, Cuicui , Wang, Boyue , Gao, Junbin , Sun, Yanfeng , Yin, Baocai . Complete/incomplete multi-view subspace clustering via soft block-diagonal-induced regulariser . | IET COMPUTER VISION , 2021 , 15 (8) , 618-632 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Low Rank Representation (LRR) based unsupervised clustering methods have achieved great success since these methods could explore low-dimensional subspace structure embedded in original data effectively. The conventional LRR methods generally treat the data as the points in Euclidean space. However, it is no longer suitable for high-dimension data (such as video or imageset). That is because high-dimension data are always considered as non-linear manifold data such as Grassmann manifold. Besides, the typical LRR methods always adopt the traditional single nuclear norm based low rank constraint which can not fully reveal the low rank property of the data representation and often leads to suboptimal solution. In this paper, a new LRR based clustering model is constructed on Grassmann manifold for high-dimension data. In the proposed method, each high-dimension data is formed as a sample on Grassmann manifold with non-linear metric. Meanwhile, a non-convex low rank representation is adopt to reveal the intrinsic property of these high-dimension data and reweighted rank minimization constraint is introduced. The experimental results on several public datasets show that the proposed method outperforms the state-of-the-art clustering methods. © 2021, Springer Nature Switzerland AG.
关键词 ：
Cluster analysis Cluster analysis Clustering algorithms Clustering algorithms Computer vision Computer vision
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| GB/T 7714 | Piao, Xinglin , Hu, Yongli , Gao, Junbin et al. Reweighted Non-convex Non-smooth Rank Minimization Based Spectral Clustering on Grassmann Manifold [C] . 2021 : 562-577 . |
| MLA | Piao, Xinglin et al. "Reweighted Non-convex Non-smooth Rank Minimization Based Spectral Clustering on Grassmann Manifold" . (2021) : 562-577 . |
| APA | Piao, Xinglin , Hu, Yongli , Gao, Junbin , Sun, Yanfeng , Yang, Xin , Yin, Baocai . Reweighted Non-convex Non-smooth Rank Minimization Based Spectral Clustering on Grassmann Manifold . (2021) : 562-577 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
基于深度学习的小目标检测方法综述
关键词 ：
特征金字塔 特征金字塔 小目标检测 小目标检测 上下文 上下文 数据增强 数据增强 深度学习 深度学习 目标检测 目标检测
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| GB/T 7714 | 员娇娇 , 胡永利 , 孙艳丰 et al. 基于深度学习的小目标检测方法综述 [J]. | 员娇娇 , 2021 , 47 (3) : 293-302 . |
| MLA | 员娇娇 et al. "基于深度学习的小目标检测方法综述" . | 员娇娇 47 . 3 (2021) : 293-302 . |
| APA | 员娇娇 , 胡永利 , 孙艳丰 , 尹宝才 , 北京工业大学学报 . 基于深度学习的小目标检测方法综述 . | 员娇娇 , 2021 , 47 (3) , 293-302 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
本发明公开了一种基于空间自注意力图卷积循环神经网络的交通数据修复方法，全连接层作为输入层将输入映射到一个高维空间提高模型的表达能力；双向图卷积门控循环单元是将门控循环单元中的全连接层替换为图卷积得到的，它能够同时建模局部空间相关性和时间相关性；多头空间自注意力模块用于捕获路网的隐含空间相关性，同时还能从全局聚合各个节点的信息；卷积层作为输出层用于对特征维度进行衰减。本发明利用图卷积建模局部空间相关性；利用门控循环单元学习交通数据的动态变化，捕获时间相关性；此外，考虑到交通状况受到许多潜在因素的影响，本发明采用多头空间自注意力机制从全局来建模交通数据的隐含空间相关性。
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| GB/T 7714 | 张勇 , 林锋 , 胡永利 et al. 一种基于空间自注意力图卷积循环神经网络的交通数据修复方法 : CN202110182167.4[P]. | 2021-02-09 . |
| MLA | 张勇 et al. "一种基于空间自注意力图卷积循环神经网络的交通数据修复方法" : CN202110182167.4. | 2021-02-09 . |
| APA | 张勇 , 林锋 , 胡永利 , 尹宝才 . 一种基于空间自注意力图卷积循环神经网络的交通数据修复方法 : CN202110182167.4. | 2021-02-09 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
本发明公开了基于多语义学习的知识图谱补全方法，将实体e1和r分别先通过多个转换矩阵学习到多个隐藏的语义表示。在前面的知识图嵌入捕捉实体和关系多个隐藏语义的步骤中，得到对同一实体或关系的多个特征嵌入。利用深度残差注意力网络优化实体和关系的嵌入。引入去噪网络优化实体嵌入和关系嵌入。接下来先简述去噪网络的结构。引入多步融合的过程来充分融合实体和关系；本发明提出来的深度残差注意力网络，能有效减少引入多个隐藏语义带来大量噪声的问题。同时去噪网络和多步融合网络能充分融合实体和关系，来得到最符合的预测结果。
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| GB/T 7714 | 尹宝才 , 王家普 , 胡永利 et al. 基于多语义学习的知识图谱补全方法 : CN202110059002.8[P]. | 2021-01-17 . |
| MLA | 尹宝才 et al. "基于多语义学习的知识图谱补全方法" : CN202110059002.8. | 2021-01-17 . |
| APA | 尹宝才 , 王家普 , 胡永利 , 孙艳丰 , 王博岳 . 基于多语义学习的知识图谱补全方法 : CN202110059002.8. | 2021-01-17 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Clustering high dimension multi-view data with complex intrinsic properties and nonlinear manifold structure is a challenging task since these data are always embedded in low dimension manifolds. Inspired by Low Rank Representation (LRR), some researchers extended classic LRR on Grassmann manifold or Product Grassmann manifold to represent data with non-linear metrics. However, most of these methods utilized convex nuclear norm to leverage a low-rank structure, which was over-relaxation of true rank and would lead to the results deviated from the true underlying ones. And, the computational complexity of singular value decomposition of matrix is high for nuclear norm minimization. In this paper, we propose a new low rank model for high-dimension multi-view data clustering on Product Grassmann Manifold with the matrix tri-factorization which is used to control the upper bound of true rank of representation matrix. And, the original problem can be transformed into the nuclear norm minimization with smaller scale matrices. An effective solution and theoretical analysis are also provided. The experimental results show that the proposed method obviously outperforms other state-of-the-art methods on several multi-source human/crowd action video datasets.
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| GB/T 7714 | Guo, Jipeng , Sun, Yanfeng , Gao, Junbin et al. Low Rank Representation on Product Grassmann Manifolds for Multi-view Subspace Clustering [C] . 2021 : 907-914 . |
| MLA | Guo, Jipeng et al. "Low Rank Representation on Product Grassmann Manifolds for Multi-view Subspace Clustering" . (2021) : 907-914 . |
| APA | Guo, Jipeng , Sun, Yanfeng , Gao, Junbin , Hu, Yongli , Yin, Baocai . Low Rank Representation on Product Grassmann Manifolds for Multi-view Subspace Clustering . (2021) : 907-914 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Locality preserving projection (LPP) is a dimensionality reduction algorithm preserving the neighhorhood graph structure of data. However, the conventional LPP is sensitive to outliers existing in data. This article proposes a novel low-rank LPP model called LR-LPP. In this new model, original data are decomposed into the clean intrinsic component and noise component. Then the projective matrix is learned based on the clean intrinsic component which is encoded in low-rank features. The noise component is constrained by the l(1)-norm which is more robust to outliers. Finally, LR-LPP model is extended to LR-FLPP in which low-dimensional feature is measured by F-norm. LR-FLPP will reduce aggregated error and weaken the effect of outliers, which will make the proposed LR-FLPP even more robust for outliers. The experimental results on public image databases demonstrate the effectiveness of the proposed LR-LPP and LR-FLPP.
关键词 ：
classification classification Dimensionality reduction Dimensionality reduction locality preserving projection locality preserving projection low rank low rank
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| GB/T 7714 | Yin, Shuai , Sun, Yanfeng , Gao, Junbin et al. Robust Image Representation via Low Rank Locality Preserving Projection [J]. | ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA , 2021 , 15 (4) . |
| MLA | Yin, Shuai et al. "Robust Image Representation via Low Rank Locality Preserving Projection" . | ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA 15 . 4 (2021) . |
| APA | Yin, Shuai , Sun, Yanfeng , Gao, Junbin , Hu, Yongli , Wang, Boyue , Yin, Baocai . Robust Image Representation via Low Rank Locality Preserving Projection . | ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA , 2021 , 15 (4) . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Traffic forecasting is attracting considerable interest due to its widespread application in intelligent transportation systems. Given the complex and dynamic traffic data, many methods focus on how to establish a spatial-temporal model to express the non-stationary traffic patterns. Recently, the latest Graph Convolution Network (GCN) has been introduced to learn spatial features while the time neural networks are used to learn temporal features. These GCN based methods obtain state-of-the-art performance. However, the current GCN based methods ignore the natural hierarchical structure of traffic systems which is composed of the micro layers of road networks and the macro layers of region networks, in which the nodes are obtained through pooling method and could include some hot traffic regions such as downtown and CBD etc., while the current GCN is only applied on the micro graph of road networks. In this paper, we propose a novel Hierarchical Graph Convolution Networks (HGC-N) for traffic forecasting by operating on both the micro and macro traffic graphs. The proposed method is evaluated on two complex city traffic speed datasets. Compared to the latest GCN based methods like Graph WaveNet, the proposed HGCN gets higher traffic forecasting precision with lower computational cost.The website of the code is https://github.com/guokan987/HGCN.git.
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| GB/T 7714 | Guo, Kan , Hu, Yongli , Sun, Yanfeng et al. Hierarchical Graph Convolution Networks for Traffic Forecasting [C] . 2021 : 151-159 . |
| MLA | Guo, Kan et al. "Hierarchical Graph Convolution Networks for Traffic Forecasting" . (2021) : 151-159 . |
| APA | Guo, Kan , Hu, Yongli , Sun, Yanfeng , Qian, Sean , Gao, Junbin , Yin, Baocai . Hierarchical Graph Convolution Networks for Traffic Forecasting . (2021) : 151-159 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
本发明公开了基于动态注意力的超网络融合视觉问答答案准确性的方法，先提取图像中两两物体之间的关系特征。通过进行关系特征的向量表示和问题文本的向量表示的余弦相似度的操作来动态的选取和问题文本相关的关系特征，并将余弦相似度分数排在前三的关系特征被选取为最为相关的关系特征；为了使视觉图片和问题文本中提取的特征融合的更加充分，提用基于超网络的卷积融合方式。利用融合图像‑问题特征学习多分类的分类器，以正确预测最佳匹配答案。使特征融合更加充分，能够使两模态之间进行深层次的交互，进一步促进视觉问答技术的准确性能的提升。
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| GB/T 7714 | 尹宝才 , 王家普 , 胡永利 et al. 基于动态注意力的超网络融合视觉问答答案准确性的方法 : CN202110182159.X[P]. | 2021-02-09 . |
| MLA | 尹宝才 et al. "基于动态注意力的超网络融合视觉问答答案准确性的方法" : CN202110182159.X. | 2021-02-09 . |
| APA | 尹宝才 , 王家普 , 胡永利 , 孙艳丰 , 王博岳 . 基于动态注意力的超网络融合视觉问答答案准确性的方法 : CN202110182159.X. | 2021-02-09 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
一种面向移动终端的轻量化开集地标识别方法，属于计算机视觉领域。本发明首先基于MobileNet‑V2轻量化网络进行改进，使其适用于地标识别任务，然后利用辅助训练集并构建新损失函数，从而提高网络的外分布异常检测能力，最后使用多项指标评估网络性能。本发明基于轻量化神经网络模型并结合外分布检测方法，使部署在移动端的模型既能排除异常图像干扰，又能高效识别任务内地标建筑，同时具备低延迟和轻量的优势。
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| GB/T 7714 | 胡永利 , 贾林涛 , 张勇 et al. 一种面向移动终端的轻量化开集地标识别方法 : CN202110184512.8[P]. | 2021-02-10 . |
| MLA | 胡永利 et al. "一种面向移动终端的轻量化开集地标识别方法" : CN202110184512.8. | 2021-02-10 . |
| APA | 胡永利 , 贾林涛 , 张勇 , 苗壮壮 , 尹宝才 . 一种面向移动终端的轻量化开集地标识别方法 : CN202110184512.8. | 2021-02-10 . |
| 导入链接 | NoteExpress RIS BibTex |
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