Query：
学者姓名：廖湖声
Refining：
Year
Type
Indexed by
Source
Complex
Co-Author
Language
Clean All
Abstract ：
Multi-view clustering, which improves clustering performance by exploring complementarity and consistency among multiple distinct feature sets, is attracting more and more researchers due to its wide applications in various fields e.g., pattern recognition and data mining. Traditional approaches usually explore above characteristics by mapping different views to a unified embedding through view-specific mapping matrices or neural networks. Then the unified embedding is fed into conventional single view clustering algorithms for final clustering results. However, a unified embedding is not enough to model distinct or even conflict multiple view characteristics due to their diverse representation abilities. Moreover, clustering and embedding learning are divided into two separate parts, which may bring in a gap between the class label and the learned embedding. To alleviate above problems, both unified and view-specific embeddings are learned, and a shared operator tensor and view-specific latent variables are introduced for their relationship modeling. Besides, a Kullback-Liebler divergence based objective is developed as a clustering oriented constraint, which leads to more clustering friendly embedding learned. Extensive experiments are conducted on six widely used datasets, achieving better results compared with several state-of-the-art approaches.
Keyword ：
Clustering friendly embedding learning Clustering friendly embedding learning Kullback-Liebler divergence Kullback-Liebler divergence Multi-view clustering Multi-view clustering Embeddings relationship modeling Embeddings relationship modeling
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Li, Yongzhen , Liao, Husheng . Diverse embeddings learning for multi-view clustering [J]. | PATTERN ANALYSIS AND APPLICATIONS , 2025 , 28 (1) . |
| MLA | Li, Yongzhen 等. "Diverse embeddings learning for multi-view clustering" . | PATTERN ANALYSIS AND APPLICATIONS 28 . 1 (2025) . |
| APA | Li, Yongzhen , Liao, Husheng . Diverse embeddings learning for multi-view clustering . | PATTERN ANALYSIS AND APPLICATIONS , 2025 , 28 (1) . |
| Export to | NoteExpress RIS BibTex |
Abstract ：
针对多视角数据间互补与一致特性难以刻画问题,提出一种基于图卷积神经网络的多视角聚类方法。通过对样本不同视角间相同邻接子图基于图卷积神经网络学习到的表达进行约束,有效挖掘了多视角数据间的一致特性。通过共享图卷积神经网络参数、学习不同视角完整邻接图嵌入表达并串接得到多视角表达,有效挖掘了多视角数据间的互补特性。对上述多视角表达增加相对熵约束,使得最终学习到的多视角表达得以提升并符合聚类特性。在五个数据集上均取得了最好的聚类效果,说明所提出的基于图卷积神经网络的聚类方法可以有效挖掘视角间互补与一致特性并提升聚类性能。
Keyword ：
相对熵 相对熵 图卷积神经网络 图卷积神经网络 多视角聚类 多视角聚类
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | 李勇振 , 廖湖声 . 基于图卷积神经网络的多视角聚类 [J]. | 计算机工程与应用 , 2021 , 57 (05) : 115-122 . |
| MLA | 李勇振 等. "基于图卷积神经网络的多视角聚类" . | 计算机工程与应用 57 . 05 (2021) : 115-122 . |
| APA | 李勇振 , 廖湖声 . 基于图卷积神经网络的多视角聚类 . | 计算机工程与应用 , 2021 , 57 (05) , 115-122 . |
| Export to | NoteExpress RIS BibTex |
Abstract ：
基于图卷积神经网络的多视角聚类
Keyword ：
图卷积神经网络 图卷积神经网络 相对熵 相对熵 多视角聚类 多视角聚类
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | 李勇振 , 廖湖声 , 计算机工程与应用 . 基于图卷积神经网络的多视角聚类 [J]. | 李勇振 , 2021 , 57 (5) : 115-122 . |
| MLA | 李勇振 等. "基于图卷积神经网络的多视角聚类" . | 李勇振 57 . 5 (2021) : 115-122 . |
| APA | 李勇振 , 廖湖声 , 计算机工程与应用 . 基于图卷积神经网络的多视角聚类 . | 李勇振 , 2021 , 57 (5) , 115-122 . |
| Export to | NoteExpress RIS BibTex |
Abstract ：
Multi-view clustering, which explores complementarity and consistency among multiple distinct feature sets to boost clustering performance, is becoming more and more useful in many real-world applications. Traditional approaches usually map multiple views to a unified embedding, in which some weighted mechanisms are utilized to measure the importance of each view. The embedding, serving as a clustering friendly representation, is then sent to extra clustering algorithms. However, a unified embedding cannot cover both complementarity and consistency among views and the weighted scheme measuring the importance of each view as a whole ignores the differences of features in each view. Moreover, because of lacking in proper grouping structure constraint imposed on the unified embedding, it will lead to just multi-view representation learned, which is not clustering friendly. In this paper, we propose a novel multi-view clustering method to alleviate the above problems. By dividing the embedding of a view into unified and view-specific vectors explicitly, complementarity and consistency can be reflected. Besides, an adversarial learning process is developed to force the above embeddings to be non-trivial. Then a fusion strategy is automatically learned, which will adaptively adjust weights for all the features in each view. Finally, a Kullback-Liebler (KL) divergence based objective is developed to constrain the fused embedding for clustering friendly representation learning and to conduct clustering. Extensive experiments have been conducted on various datasets, performing better than the state-of-the-art clustering approaches.
Keyword ：
Clustering friendly representation learning Clustering friendly representation learning Multi-view clustering Multi-view clustering Adaptive view fusion Adaptive view fusion Adversarial view embedding Adversarial view embedding
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Li, Yongzhen , Liao, Husheng . Multi-view clustering via adversarial view embedding and adaptive view fusion [J]. | APPLIED INTELLIGENCE , 2020 , 51 (3) : 1201-1212 . |
| MLA | Li, Yongzhen 等. "Multi-view clustering via adversarial view embedding and adaptive view fusion" . | APPLIED INTELLIGENCE 51 . 3 (2020) : 1201-1212 . |
| APA | Li, Yongzhen , Liao, Husheng . Multi-view clustering via adversarial view embedding and adaptive view fusion . | APPLIED INTELLIGENCE , 2020 , 51 (3) , 1201-1212 . |
| Export to | NoteExpress RIS BibTex |
Abstract ：
This paper proposes a new kind of pushdown transducer ExPDT to do XML data stream evaluation effectively and efficiently for XPath queries with parallel predicates, multilevel nested predicates and predicates with value. Compared with relative researches, ExPDT expands the support of XPath queries, and the experiment shows that it improves the performance of XML data stream evaluation. © 2019 IEEE.
Keyword ：
Data streams Data streams XML XML Transducers Transducers
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Jin, Xueyun , Liao, Husheng . ExPDT: An extended pushdown transducer for xml stream processing [C] . 2019 : 1048-1052 . |
| MLA | Jin, Xueyun 等. "ExPDT: An extended pushdown transducer for xml stream processing" . (2019) : 1048-1052 . |
| APA | Jin, Xueyun , Liao, Husheng . ExPDT: An extended pushdown transducer for xml stream processing . (2019) : 1048-1052 . |
| Export to | NoteExpress RIS BibTex |
Abstract ：
复杂事件处理技术是流数据查询的常见手段，随着数据多元化的发展，查询的复杂度急剧提升，其处理性能面临极大地挑战。为了提高多源复杂事件查询的处理效率，本文提出一种新的查询分解方案，提高集群资源利用率；针对事件源匹配，将查询中的筛选与事件源匹配中的筛选相结合设计了专用优化方案。实验证明以上方法能够有效地提高复杂事件查询效率。
Keyword ：
Pattern Match Pattern Match Query Optimization Query Optimization 查询优化Complex Event Processing 查询优化Complex Event Processing 复杂事件处理 复杂事件处理 模式匹配 模式匹配
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | 苏畅 , 廖湖声 , 高红雨 et al. 多源复杂事件检测中查询计划生成与优化技术的研究 [J]. | 软件工程与应用 , 2018 , 7 (02) : 75-83 . |
| MLA | 苏畅 et al. "多源复杂事件检测中查询计划生成与优化技术的研究" . | 软件工程与应用 7 . 02 (2018) : 75-83 . |
| APA | 苏畅 , 廖湖声 , 高红雨 , 张嘉伟 . 多源复杂事件检测中查询计划生成与优化技术的研究 . | 软件工程与应用 , 2018 , 7 (02) , 75-83 . |
| Export to | NoteExpress RIS BibTex |
Abstract ：
随着对半结构化流式数据进行复杂事件查询的需求日益增加,高效地进行复杂事件查询显得尤为重要.目前针对复杂事件查询的方法主要集中在仅有结构约束的查询请求,对同时含有时序约束的查询请求不能很好地支持.因此,针对XML这种半结构化流式数据,提出了一种基于下推自动机扩展的模式匹配算法,它能够高效地处理使用正规树模式描述的含有结构约束和时序约束的复杂事件,通过对比实验也证明了该方法具有更高的性能.
Keyword ：
正规树模式 正规树模式 XML流数据 XML流数据 下推自动机 下推自动机 复杂事件查询 复杂事件查询
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | 郑利强 , 廖湖声 , 苏航 et al. 一种针对正规树模式的复杂事件查询方法 [J]. | 计算机与数字工程 , 2018 , 46 (5) : 966-971 . |
| MLA | 郑利强 et al. "一种针对正规树模式的复杂事件查询方法" . | 计算机与数字工程 46 . 5 (2018) : 966-971 . |
| APA | 郑利强 , 廖湖声 , 苏航 , 高红雨 . 一种针对正规树模式的复杂事件查询方法 . | 计算机与数字工程 , 2018 , 46 (5) , 966-971 . |
| Export to | NoteExpress RIS BibTex |
Abstract ：
针对流数据在线实时到达,顺序性一次访问及处理时效性高、缓存量小的需求,提出一种基于森林自动机处理XPath查询的方法.定义XPath查询到森林自动机实例的转换规则;采用栈结构和抽象语法树相结合的方式,不断接收流数据结点,驱动自动机的运行,完成结点匹配和状态转换动作;在抽象语法树中维护各状态函数之间的关系及中间结果,归约过程中获得查询结果随即输出.实验结果验证了该方法处理流数据的有效性,在标准测试数据集下,与同类方法和引擎相比,在处理效率上有近30％的提高,内存占接近于常量,较好解决了时空复杂度平衡问题,为其它方法提供了有益的参考.
Keyword ：
森林自动机 森林自动机 查询处理 查询处理 XML数据 XML数据 流数据 流数据 XPath查询 XPath查询
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | 何志学 , 廖湖声 . 基于森林自动机处理XML流数据方法 [J]. | 计算机工程与设计 , 2018 , 39 (10) : 3092-3099 . |
| MLA | 何志学 et al. "基于森林自动机处理XML流数据方法" . | 计算机工程与设计 39 . 10 (2018) : 3092-3099 . |
| APA | 何志学 , 廖湖声 . 基于森林自动机处理XML流数据方法 . | 计算机工程与设计 , 2018 , 39 (10) , 3092-3099 . |
| Export to | NoteExpress RIS BibTex |
Abstract ：
基于trace的即时编译技术是一种提高解释型语言性能的有效方法.然而,现有的trace探测技术都是针对程序单次执行的,无法利用服务器端程序并发执行的特点.针对并发执行的服务器端程序,提出一种基于序列模式挖掘的trace探测方法,以快速发现热点trace.将并发执行的服务器端程序看作是多个基本块序列,应用序列模式挖掘算法,对得到的序列模式进行去重与合并以发现热点trace.实验结果表明基于序列模式挖掘的trace探测能够有效地提高trace探测的效率.
Keyword ：
基于trace 基于trace 即时编译 即时编译 序列模式挖掘 序列模式挖掘
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | 潘龙 , 廖湖声 , 苏航 . 一种基于序列模式挖掘的trace探测方法 [J]. | 计算机应用与软件 , 2018 , 35 (7) : 1-7,14 . |
| MLA | 潘龙 et al. "一种基于序列模式挖掘的trace探测方法" . | 计算机应用与软件 35 . 7 (2018) : 1-7,14 . |
| APA | 潘龙 , 廖湖声 , 苏航 . 一种基于序列模式挖掘的trace探测方法 . | 计算机应用与软件 , 2018 , 35 (7) , 1-7,14 . |
| Export to | NoteExpress RIS BibTex |
Abstract ：
近年来,随着大数据时代的到来,大数据处理平台发展迅速,产生了诸如Hadoop,Spark,Storm等优秀的大数据处理平台,其中Spark最为突出。随着Spark在国内外的广泛应用,其许多性能问题尚待解决。由于Spark底层的执行机制极为复杂,用户很难找到其性能瓶颈,更不要说进一步的优化。针对以上问题,从开发原则优化、内存优化、配置参数优化、调度优化、Shuffle过程优化5个方面对目前国内外的Spark优化技术进行总结和分析。最后,总结了目前Spark优化技术新的核心问题,并提出了未来的主要研究方向。
Keyword ：
内存优化 内存优化 调度优化 调度优化 开发原则优化 开发原则优化 Shuffle过程优化 Shuffle过程优化 参数优化 参数优化 Spark Spark
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | 廖湖声 , 黄珊珊 , 徐俊刚 et al. Spark性能优化技术研究综述 [J]. | 计算机科学 , 2018 , 45 (07) : 7-15,37 . |
| MLA | 廖湖声 et al. "Spark性能优化技术研究综述" . | 计算机科学 45 . 07 (2018) : 7-15,37 . |
| APA | 廖湖声 , 黄珊珊 , 徐俊刚 , 刘仁峰 . Spark性能优化技术研究综述 . | 计算机科学 , 2018 , 45 (07) , 7-15,37 . |
| Export to | NoteExpress RIS BibTex |
Export
| Results： |
Selected to |
| Format： |
