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学者姓名：孙艳丰
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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 ：
随着信息技术的高速发展,各种数字档案数据量出现了爆炸式的增长。如何合理地挖掘分析档案数据,提升对新收录档案智能管理的效果已成为一个亟需解决的问题。现有的档案数据分类方法是面向管理需求的人工分类,这种人工分类的方式效率低下,忽略了档案固有的内容信息。此外,对于档案信息发现和利用来说,需进一步挖掘分析档案数据内容之间的关联性。面向档案智能管理的需求,从档案数据的文本内容角度出发,对人工分类的档案进行进一步分析。采用LDA模型提取文档的主题特征向量,进而用K-means算法对档案的主题特征进行聚类,得到档案间的关联。针对新收录档案数据的分类问题,采用现有档案数据,有监督的训练FastText深度学习...
关键词 ：
FastText文本分类 FastText文本分类 档案管理 档案管理 LDA特征表示 LDA特征表示 文本聚类 文本聚类
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| GB/T 7714 | 霍光煜 , 张勇 , 孙艳丰 et al. 基于语义的档案数据智能分类方法研究 [J]. | 计算机工程与应用 , 2021 , 57 (06) : 247-253 . |
| MLA | 霍光煜 et al. "基于语义的档案数据智能分类方法研究" . | 计算机工程与应用 57 . 06 (2021) : 247-253 . |
| APA | 霍光煜 , 张勇 , 孙艳丰 , 尹宝才 . 基于语义的档案数据智能分类方法研究 . | 计算机工程与应用 , 2021 , 57 (06) , 247-253 . |
| 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 ：
基于语义的档案数据智能分类方法研究
关键词 ：
文本聚类 文本聚类 档案管理 档案管理 FastText文本分类 FastText文本分类 LDA特征表示 LDA特征表示
引用：
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| GB/T 7714 | 霍光煜 , 张勇 , 孙艳丰 et al. 基于语义的档案数据智能分类方法研究 [J]. | 霍光煜 , 2021 , 57 (6) : 247-253 . |
| MLA | 霍光煜 et al. "基于语义的档案数据智能分类方法研究" . | 霍光煜 57 . 6 (2021) : 247-253 . |
| APA | 霍光煜 , 张勇 , 孙艳丰 , 尹宝才 , 计算机工程与应用 . 基于语义的档案数据智能分类方法研究 . | 霍光煜 , 2021 , 57 (6) , 247-253 . |
| 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 ：
Sparse representation based on over-complete dictionaries is a hot issue in the field of computer vision and machine learning. In probability theory, over-complete dictionary can be learned by non-parametric Bayesian techniques with Beta Process. However, traditional probabilistic dictionary learning method assumes noise follows Gaussian distribution, which can only remove Gaussain noise. In order to remove outlier or complex noise, we propose a dictionary learning method based on non-parametric Bayesian technology by assuming the noise follows Laplacian distribution. Because the non-conjugacy of Laplacian distribution makes the calculation of posteriors of latent variables more complicate, thus we utilize a superposition of an infinite number of Gaussian distributions to substitute for L1 density function. The weights of mixture Gaussian distribution are controlled by an extra hidden variable. Then the Bayesian inference is applied to learn all the key parameters in the proposed probabilistic model, which avoids the processing of parameter setting and fine tuning. In the experiments, we mainly test the performance of different algorithms in removing salt-and-pepper noise and mixture noises. The experimental results show that the PSNRs of our algorithm are higher 2-4 dB at least than other classic algorithms.
关键词 ：
Variational inference Variational inference Sparse representation Sparse representation Dictionary learning Dictionary learning Image denosing Image denosing
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| GB/T 7714 | Ju, Fujiao , Sun, Yanfeng , Li, Mingyang . Non-parametric Bayesian dictionary learning based on Laplace noise [J]. | MULTIMEDIA TOOLS AND APPLICATIONS , 2021 . |
| MLA | Ju, Fujiao et al. "Non-parametric Bayesian dictionary learning based on Laplace noise" . | MULTIMEDIA TOOLS AND APPLICATIONS (2021) . |
| APA | Ju, Fujiao , Sun, Yanfeng , Li, Mingyang . Non-parametric Bayesian dictionary learning based on Laplace noise . | MULTIMEDIA TOOLS AND APPLICATIONS , 2021 . |
| 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 ：
针对理工科课程在教学内容、方式上实施"润物细无声"式课程思政的难度,以计算机图形学为例,提出课程思政建设总体思路,在阐述具体课程思政建设过程及结果的基础上,凝练总体建设原则,给出一般化的理工科课程思政建设策略,为在理工科院校更广泛、更有效地开展课程思政建设提供思路。
关键词 ：
内涵与外延 内涵与外延 理工科课程思政建设 理工科课程思政建设 科学方法论 科学方法论 计算机图形学 计算机图形学 课程思政 课程思政
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| GB/T 7714 | 孔德慧 , 李敬华 , 王立春 et al. 基于计算机图形学教学实践的理工科课程思政建设研究 [J]. | 计算机教育 , 2021 , (09) : 15-18 . |
| MLA | 孔德慧 et al. "基于计算机图形学教学实践的理工科课程思政建设研究" . | 计算机教育 09 (2021) : 15-18 . |
| APA | 孔德慧 , 李敬华 , 王立春 , 张勇 , 孙艳丰 . 基于计算机图形学教学实践的理工科课程思政建设研究 . | 计算机教育 , 2021 , (09) , 15-18 . |
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