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Supervoxel-based graph clustering for Accurate Object Segmentation of Indoor Point Clouds

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Supervoxel-based Graph Clustering for Accurate Object Segmen...

SMEC:Scene Mining for E-Commerce

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作者： Jingyuan Yu Xiaowei Tu Qinghua Yang Liyong Liu School of Mechanical Engineering and Automation Shanghai University

The correct segmentation of clustered objects in indoor scenes is a critical step of machine understanding in the field of robotics. However, traditional segmentation methods can hardly meet the requirements of accuracy and real time. To this end, an unsupervised object segmentation method based on supervoxel and graph clustering is proposed in this paper. First, the background is removed based on RANSAC, and the remaining point cloud is over-segmented into supervoxels, which is used to improve the segmentation efficiency and to obtain fully connected graph. Second, a graph clustering method based on pure geometric features is proposed to obtain the corresponding subgraphs of each object using the concavity between ***, a new refinement method for processing the boundaries at the intersection is adapted for obtaining the boundaries with point-level accuracy to improve the segmentation accuracy. The method is experimented on both researcher community public and self-collected datasets. Qualitative and quantitative results show the applicability of our method to robotic real-time object manipulation and its broad development prospect.

关键词： Object Segmentation Point Cloud Supervoxels graph clustering

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Journal of Computer Science & Technology 2024年第1期39卷 192-210页

作者：王罡李翔郭子义殷大伟马帅 State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China School of Data Science and Engineering East China Normal UniversityShanghai 200062China *** Inc.Beijing 100176China Baidu Inc.Beijing 100085China

Scene-based recommendation has proven its usefulness in E-commerce,by recommending commodities based on a given ***,scenes are typically unknown in advance,which necessitates scene discovery for *** this article,we study scene discovery for E-commerce *** first formalize a scene as a set of commodity cate-gories that occur simultaneously and frequently in real-world situations,and model an E-commerce platform as a heteroge-neous information network(HIN),whose nodes and links represent different types of objects and different types of rela-tionships between objects,*** then formulate the scene mining problem for E-commerce as an unsupervised learning problem that finds the overlapping clusters of commodity categories in the *** solve the problem,we pro-pose a non-negative matrix factorization based method SMEC(Scene Mining for E-Commerce),and theoretically prove its *** six real-world E-commerce datasets,we finally conduct an extensive experimental study to evaluate SMEC against 13 other methods,and show that SMEC consistently outperforms its competitors with regard to various evaluation measures.

关键词： graph clustering E-commerce heterogeneous information network(HIN) scene mining

A Study of Triangle Inequality Violations in Social Network clustering

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Journal of Computer and Communications 2024年第1期12卷 67-76页

作者： Sanjit Kumar Saha Tapashi Gosswami Department of Computer Science and Engineering Jahangirnagar University Dhaka Bangladesh Brandenburg University of Technology Cottbus-Senftenberg Cottbus Germany

clustering a social network is a process of grouping social actors into clusters where intra-cluster similarities among actors are higher than inter-cluster similarities. clustering approaches, i.e. , k-medoids or hierarchical, use the distance function to measure the dissimilarities among actors. These distance functions need to fulfill various properties, including the triangle inequality (TI). However, in some cases, the triangle inequality might be violated, impacting the quality of the resulting clusters. With experiments, this paper explains how TI violates while performing traditional clustering techniques: k-medoids, hierarchical, DENgraph, and spectral clustering on social networks and how the violation of TI affects the quality of the resulting clusters.

关键词： clustering Triangle Inequality Violations Traditional clustering graph clustering

graph SPARSIFICATION BY UNIVERSAL GREEDY ALGORITHMS

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Journal of Computational Mathematics 2023年第4期41卷 741-770页

作者： Ming-Jun Lai Jiaxin Xie Zhiqiang Xu Department of Mathematics University of GeorgiaAthensGA 30602USA LMIB of the Ministry of Education School of Mathematical SciencesBeihang UniversityBeijing 100191China LSEC Inst.Comp.Math.Academy of Mathematics and System ScienceChinese Academy of SciencesBeijing 100091China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China

graph sparsification is to approximate an arbitrary graph by a sparse graph and is useful in many applications,such as simplification of social networks,least squares problems,and numerical solution of symmetric positive definite linear *** this paper,inspired by the well-known sparse signal recovery algorithm called orthogonal matching pursuit(OMP),we introduce a deterministic,greedy edge selection algorithm,which is called the universal greedy approach(UGA)for the graph sparsification *** a general spectral sparsification problem,e.g.,the positive subset selection problem from a set of m vectors in R n,we propose a nonnegative UGA algorithm which needs O(mn^(2)+n^(3)/ϵ^(2))time to find a 1+ϵ/β/1-ϵ/β-spectral sparsifier with positive coefficients with sparsity at most[n/ϵ^(2)],where β is the ratio between the smallest length and largest length of the *** convergence of the nonnegative UGA algorithm is *** the graph sparsification problem,another UGA algorithm is proposed which can output a 1+O(ϵ)/1-O(ϵ)-spectral sparsifier with[n/ϵ^(2)]edges in O(m+n^(2)/ϵ^(2))time from a graph with m edges and n vertices under some mild *** is a linear time algorithm in terms of the number of edges that the community of graph sparsification is looking *** best result in the literature to the knowledge of the authors is the existence of a deterministic algorithm which is almost linear,i.e.O(m^(1+o(1)))for some o(1)=O((log log(m))^(2/3)/log^(1/3)(m)).Finally,extensive experimental results,including applications to graph clustering and least squares regression,show the effectiveness of proposed approaches.

关键词： Spectral sparsification Subset selection Greedy algorithms graph clustering Linear sketching

A framework combines supervised learning and dense subgraphs discovery to predict protein complexes

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Frontiers of Computer Science 2022年第1期16卷 173-186页

作者： Suyu MEI Software College Shenyang Normal UniversityShenyang110034China

Rapidly identifying protein complexes is significant to elucidate the mechanisms of macromolecular interactions and to further investigate the overlapping clinical manifestations of *** date,existing computational methods majorly focus on developing unsupervised graph clustering algorithms,sometimes in combination with prior biological insights,to detect protein complexes from protein-protein interaction(PPI)***,the outputs of these methods are potentially structural or functional modules within PPI *** modules do not necessarily correspond to the actual protein complexes that are formed via spatiotemporal aggregation of *** this study,we propose a computational framework that combines supervised learning and dense subgraphs discovery to predict protein *** proposed framework consists of two *** first step reconstructs genome-scale protein co-complex networks via training a supervised learning model of l2-regularized logistic regression on experimentally derived co-complexed protein pairs;and the second step infers hierarchical and balanced clusters as complexes from the co-complex networks via effective but computationally intensive k-clique graph clustering method or efficient maximum modularity clustering(MMC)*** studies of cross validation and independent test show that both steps achieve encouraging *** proposed framework is fundamentally novel and excels over existing methods in that the complexes inferred from protein co-complex networks are more biologically relevant than those inferred from PPI networks,providing a new avenue for identifying novel protein complexes.

关键词： protein complexes protein co-complex networks machine learning L2-regularized logistic regression graph clustering

A Dimensionality Reduction Framework for Detection of Multiscale Structure in Heterogeneous Networks

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Journal of Computer Science & Technology 2012年第2期27卷 341-357页

作者：沈华伟程学旗王元卓陈一昕 Institute of Computing Technology Chinese Academy of Sciences Computer Science Department Washington University in St Louis

graph clustering has been widely applied in exploring regularities emerging in relational ***,the rapid development of network theory correlates graph clustering with the detection of community structure,a common and important topological characteristic of *** existing methods investigate the community structure at a single topological ***,as shown by empirical studies,the community structure of real world networks often exhibits multiple topological descriptions,corresponding to the clustering at different ***,the detection of multiscale community structure is heavily affected by the heterogeneous distribution of node *** is very challenging to detect multiscale community structure in heterogeneous *** this paper,we propose a novel,unified framework for detecting community structure from the perspective of dimensionality *** on the framework,we first prove that the well-known Laplacian matrix for network partition and the widely-used modularity matrix for community detection are two kinds of covariance matrices used in dimensionality *** then propose a novel method to detect communities at multiple topological scales within our *** further show that existing algorithms fail to deal with heterogeneous node *** develop a novel method to handle heterogeneity of networks by introducing a rescaling transformation into the covariance matrices in our *** tests on real world and artificial networks demonstrate that the proposed correlation matrices significantly outperform Laplacian and modularity matrices in terms of their ability to identify multiscale community structure in heterogeneous networks.

关键词： graph clustering community structure Laplacian matrix modularity matrix dimensionality reduction

CPL:Detecting Protein Complexes by Propagating Labels on Protein-Protein Interaction Network

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Journal of Computer Science & Technology 2014年第6期29卷 1083-1093页

作者：代启国郭茂祖刘晓燕滕志霞王春宇 School of Computer Science and Technology Harbin Institute of Technology School of Information and Computer Engineering Northeast Forestry University

Proteins usually bind together to form complexes, which play an important role in cellular activities. Many graph clustering methods have been proposed to identify protein complexes by finding dense regions in protein-protein interaction networks. We present a novel framework （CPL） that detects protein complexes by propagating labels through interactions in a network, in which labels denote complex identifiers. With proper propagation in CPL, proteins in the same complex will be assigned with the same labels. CPL does not make any strong assumptions about the topological structures of the complexes, as in previous methods. Tile CPL algorithm is tested on several publicly available yeast protein-protein interaction networks and compared with several state-of-the-art methods. The results suggest that CPL performs better than the existing methods. An analysis of the functional homogeneity based on a gene ontology analysis shows that the detected complexes of CPL are highly biologically relevant.

关键词： protein complex detection label propagation protein-protein interaction graph clustering bioinformatics