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1.
《Information processing & management》2022,59(4):102967
Graph-based multi-view clustering aims to take advantage of multiple view graph information to provide clustering solutions. The consistency constraint of multiple views is the key of multi-view graph clustering. Most existing studies generate fusion graphs and constrain multi-view consistency by clustering loss. We argue that local pair-view consistency can achieve fine-modeling of consensus information in multiple views. Towards this end, we propose a novel Contrastive and Attentive Graph Learning framework for multi-view clustering (CAGL). Specifically, we design a contrastive fine-modeling in multi-view graph learning using maximizing the similarity of pair-view to guarantee the consistency of multiple views. Meanwhile, an Att-weighted refined fusion graph module based on attention networks to capture the capacity difference of different views dynamically and further facilitate the mutual reinforcement of single view and fusion view. Besides, our CAGL can learn a specialized representation for clustering via a self-training clustering module. Finally, we develop a joint optimization objective to balance every module and iteratively optimize the proposed CAGL in the framework of graph encoder–decoder. Experimental results on six benchmarks across different modalities and sizes demonstrate that our CAGL outperforms state-of-the-art baselines. 相似文献
2.
《Information processing & management》2023,60(3):103284
Deep multi-view clustering (MVC) is to mine and employ the complex relationships among views to learn the compact data clusters with deep neural networks in an unsupervised manner. The more recent deep contrastive learning (CL) methods have shown promising performance in MVC by learning cluster-oriented deep feature representations, which is realized by contrasting the positive and negative sample pairs. However, most existing deep contrastive MVC methods only focus on the one-side contrastive learning, such as feature-level or cluster-level contrast, failing to integrating the two sides together or bringing in more important aspects of contrast. Additionally, most of them work in a separate two-stage manner, i.e., first feature learning and then data clustering, failing to mutually benefit each other. To fix the above challenges, in this paper we propose a novel joint contrastive triple-learning framework to learn multi-view discriminative feature representation for deep clustering, which is threefold, i.e., feature-level alignment-oriented and commonality-oriented CL, and cluster-level consistency-oriented CL. The former two submodules aim to contrast the encoded feature representations of data samples in different feature levels, while the last contrasts the data samples in the cluster-level representations. Benefiting from the triple contrast, the more discriminative representations of views can be obtained. Meanwhile, a view weight learning module is designed to learn and exploit the quantitative complementary information across the learned discriminative features of each view. Thus, the contrastive triple-learning module, the view weight learning module and the data clustering module with these fused features are jointly performed, so that these modules are mutually beneficial. The extensive experiments on several challenging multi-view datasets show the superiority of the proposed method over many state-of-the-art methods, especially the large improvement of 15.5% and 8.1% on Caltech-4V and CCV in terms of accuracy. Due to the promising performance on visual datasets, the proposed method can be applied into many practical visual applications such as visual recognition and analysis. The source code of the proposed method is provided at https://github.com/ShizheHu/Joint-Contrastive-Triple-learning. 相似文献
3.
《Information processing & management》2016,52(4):618-627
Cluster analysis using multiple representations of data is known as multi-view clustering and has attracted much attention in recent years. The major drawback of existing multi-view algorithms is that their clustering performance depends heavily on hyperparameters which are difficult to set. In this paper, we propose the Multi-View Normalized Cuts (MVNC) approach, a two-step algorithm for multi-view clustering. In the first step, an initial partitioning is performed using a spectral technique. In the second step, a local search procedure is used to refine the initial clustering. MVNC has been evaluated and compared to state-of-the-art multi-view clustering approaches using three real-world datasets. Experimental results have shown that MVNC significantly outperforms existing algorithms in terms of clustering quality and computational efficiency. In addition to its superior performance, MVNC is parameter-free which makes it easy to use. 相似文献
4.
《Information processing & management》2022,59(5):103003
Many methods of multi-kernel clustering have a bias to power base kernels by ignoring other kernels. To address this issue, in this paper, we propose a new method of multi-kernel graph fusion based on min–max optimization (namely MKGF-MM) for spectral clustering by making full use of all base kernels. Specifically, the proposed method investigates a novel min–max weight strategy to capture the complementary information among all base kernels. As a result, every base kernel contributes to the construction of the fusion graph from all base kernels so that the quality of the fusion graph is guaranteed. In addition, we design an iterative optimization method to solve the proposed objective function. Furthermore, we theoretically prove that our optimization method achieves convergence. Experimental results on real medical datasets and scientific datasets demonstrate that the proposed method outperforms all comparison methods and the proposed optimization method achieves fast convergence. 相似文献
5.
Hong Zhu Chuan Chen Li-Zhi Liao Michael K. Ng 《Journal of The Franklin Institute》2018,355(4):1819-1845
The core issue of multiple graphs clustering is to find clusters of vertices from graphs such that these clusters are well-separated in each graph and clusters are consistent across different graphs. The problem can be formulated as a multiple orthogonality constrained optimization model which can be shown to be a relaxation of a multiple graphs cut problem. The resulting optimization problem can be solved by a gradient flow iterative method. The convergence of the proposed iterative scheme can be established. Numerical examples are presented to demonstrate the effectiveness of the proposed method for solving multiple graphs clustering problems in terms of clustering accuracy and computational efficiency. 相似文献
6.
基于改进特征提取及聚类的网络评论挖掘研究 总被引：1，自引：0，他引：1
[目的/意义]针对信息过载条件下中文网络产品评论中特征提取性能低以及特征聚类中初始中心点的选取问题。[方法/过程]本研究提出采用基于权重的改进Apriori算法产生候选产品特征集合,再根据独立支持度、频繁项名词非特征规则及基于网络搜索引擎的PMI算法对候选产品特征集合进行过滤。并以基于HowNet的语义相似度和特征观点共现作为衡量产品特征之间关联程度的特征,提出一种改进K-means聚类算法对产品特征进行聚类。[结果/结论]实验结果表明,在特征提取阶段,查准率为69%,查全率为92.64%,综合值达到79.07%。在特征聚类阶段,本文提出的改进K-means算法相对传统算法具有更优的挖掘性能。 相似文献
7.
《Information processing & management》2005,41(2):177-194
In this paper, we propose a re-ranking algorithm using post-retrieval clustering for content-based image retrieval (CBIR). In conventional CBIR systems, it is often observed that images visually dissimilar to a query image are ranked high in retrieval results. To remedy this problem, we utilize the similarity relationship of the retrieved results via post-retrieval clustering. In the first step of our method, images are retrieved using visual features such as color histogram. Next, the retrieved images are analyzed using hierarchical agglomerative clustering methods (HACM) and the rank of the results is adjusted according to the distance of a cluster from a query. In addition, we analyze the effects of clustering methods, query-cluster similarity functions, and weighting factors in the proposed method. We conducted a number of experiments using several clustering methods and cluster parameters. Experimental results show that the proposed method achieves an improvement of retrieval effectiveness of over 10% on average in the average normalized modified retrieval rank (ANMRR) measure. 相似文献
8.
Lei Cai Jianqing Zhu Huanqiang Zeng Jing Chen Canhui Cai Kai-Kuang Ma 《Journal of The Franklin Institute》2018,355(4):1991-2008
Pedestrian gender recognition is a very challenging problem, since the viewpoint variations, illumination changes, occlusion, and poor quality are usually encountered in the pedestrian images. To address this problem, an effective HOG-assisted deep feature learning (HDFL) method is proposed in this paper. The key novelty lies in the design of HDFL network to effectively explore both deep-learned feature and weighted histogram of oriented gradient (HOG) feature for the pedestrian gender recognition. Specifically, the deep-learned and weighted HOG feature extraction branches are simultaneously performed on the input pedestrian image. A feature fusion process is subsequently conducted to obtain a more robust and discriminative feature, which is then fed to a softmax classifier for pedestrian gender recognition. Extensive experiments on multiple existing pedestrian image datasets have shown that the proposed HDFL method is able to effectively recognize the pedestrian gender, and consistently outperforms the state-of-the-art methods. 相似文献
9.
Typically graph-clustering approaches assume that a cluster is a vertex subset such that for all of its vertices, the number of links connecting a vertex to its cluster is higher than the number of links connecting the vertex to the remaining graph. We consider a cluster such that for all of its vertices, the number of links connecting a vertex to its cluster is higher than the number of links connecting the vertex to any other cluster. Based on this fundamental view, we propose a graph-clustering algorithm that identifies clusters even if they contain vertices more strongly connected outside than inside their cluster; hence, the proposed algorithm is proved exceptionally efficient in clustering densely interconnected graphs. Extensive experimentation with artificial and real datasets shows that our approach outperforms earlier alternate clustering techniques. 相似文献
10.
This paper presents a cluster validation based document clustering algorithm, which is capable of identifying an important feature subset and the intrinsic value of model order (cluster number). The important feature subset is selected by optimizing a cluster validity criterion subject to some constraint. For achieving model order identification capability, this feature selection procedure is conducted for each possible value of cluster number. The feature subset and the cluster number which maximize the cluster validity criterion are chosen as our answer. We have evaluated our algorithm using several datasets from the 20Newsgroup corpus. Experimental results show that our algorithm can find the important feature subset, estimate the cluster number and achieve higher micro-averaged precision than previous document clustering algorithms which require the value of cluster number to be provided. 相似文献
11.
《Journal of The Franklin Institute》2017,354(11):4457-4480
Due to the hopeful application of gathering information from unreachable position, wireless sensor network creates an immense challenge for data routing to maximize the communication with more energy efficiency. In order to design the energy efficient routing, the optimization based clustering protocols are more preferred in wireless sensor network. In this paper, we have proposed competent optimization based algorithm called Fractional lion (FLION) clustering algorithm for creating the energy efficient routing path. Here, the proposed clustering algorithm is used to increase the energy and lifetime of the network nodes by selecting the rapid cluster head. In addition, we have proposed multi-objective FLION clustering algorithm to develop the new fitness function based on the five objectives like intra-cluster distance, inter-cluster distance, cluster head energy, normal nodes energy and delay. Here, the proposed fitness function is used to find the rapid cluster centroid for an efficient routing path. Finally, the performance of the proposed clustering algorithm is compared with the existing clustering algorithms such as low energy adaptive clustering hierarchy (LEACH), particle swarm optimization (PSO), artificial bee colony (ABC) and Fractional ABC clustering algorithm. The results proved that the lifetime of the wireless sensor nodes is maximized by the proposed FLION based multi-objective clustering algorithm as compared with existing protocols. 相似文献
12.
《Information processing & management》2020,57(2):102180
Search task success rate is an important indicator to measure the performance of search engines. In contrast to most of the previous approaches that rely on labeled search tasks provided by users or third-party editors, this paper attempts to improve the performance of search task success evaluation by exploiting unlabeled search tasks that are existing in search logs as well as a small amount of labeled ones. Concretely, the Multi-view Active Semi-Supervised Search task Success Evaluation (MA4SE) approach is proposed, which exploits labeled data and unlabeled data by integrating the advantages of both semi-supervised learning and active learning with the multi-view mechanism. In the semi-supervised learning part of MA4SE, we employ a multi-view semi-supervised learning approach that utilizes different parameter configurations to achieve the disagreement between base classifiers. The base classifiers are trained separately from the pre-defined action and time views. In the active learning part of MA4SE, each classifier received from semi-supervised learning is applied to unlabeled search tasks, and the search tasks that need to be manually annotated are selected based on both the degree of disagreement between base classifiers and a regional density measurement. We evaluate the proposed approach on open datasets with two different definitions of search tasks success. The experimental results show that MA4SE outperforms the state-of-the-art semi-supervised search task success evaluation approach. 相似文献
13.
《Information processing & management》2020,57(4):102240
Multi-label classification (MLC) has attracted many researchers in the field of machine learning as it has a straightforward problem statement with varied solution approaches. Multi-label classifiers predict multiple labels for a single instance. The problem becomes challenging with the increasing number of features, especially when there are many features and labels which depend on each other. It requires dimensionality reduction before applying any multi-label learning method. This paper introduces a method named FS-MLC (Feature Selection forMulti-Label classification using Clustering in feature-space). It is a wrapper feature selection method that uses clustering to find the similarity among features and example-based precision and recall as the metrics for feature rankings to improve the performance of the associated classifier in terms of sample-based measures. First, clusters are created for features considering them as instances then features from different clusters are selected as the representative of all the features for that cluster. It reduces the number of features as a single feature represents multiple features within a cluster. It neither requires any parameter tuning nor the user threshold for the number of features selected. Extensive experimentation is performed to evaluate the efficacy of these reduced features using nine benchmark MLC datasets on twelve performance measures. The results show an impressive improvement in terms of sample-based precision, recall, and f1-score with up to 23%-93% discarded features. 相似文献
14.
本文采取了3种必要的措施提高了聚类质量:考虑到各维数据特征属性对聚类效果影响不同,采用了基于统计方法的维度加权的方法进行特征选择;对于和声搜索算法的调音概率进行了改进,将改进的和声搜索算法和模糊聚类相结合用于快速寻找最优的聚类中心;循环测试各种中心数情况下的聚类质量以获得最佳的类中心数。接着,该算法被应用于图书馆读者兴趣度建模中,用于识别图书馆日常运行时各读者借阅图书的类型,实验表明该算法较其它算法更优。这样的读者兴趣度聚类分析可以进行图书推荐,从而提高图书馆的运行效率。 相似文献
15.
大数据环境下,推荐系统项目评分的稀疏性问题愈加突出,单兴趣表示方法也难以对用户多种情境兴趣进行准确描述,导致推荐结果精度大大降低。鉴于此,提出一种多情境兴趣表示方法,在此基础上构建面向图书馆大数据知识服务的多情境兴趣推荐模型,通过对用户多情境兴趣的层次划分,利用蚁群层次挖掘的优势来发现目标用户的若干最近邻类簇,然后根据类簇内相似用户对目标项目的评分对未评分项目进行预测,最后结合MapReduce化的大数据并行处理方法来进行协同过滤推荐。实验结果表明,多情境兴趣的建模方法改善了单兴趣建模存在的歧义推荐问题,有效缓解了大数据环境下项目评分的数据稀疏问题,MapReduce化的蚁群层次聚类方法也大大改善了推荐系统的运行效率。 相似文献
16.
《Information processing & management》2023,60(6):103479
In recent years, Zero-shot Node Classification (ZNC), an emerging and more difficult task is starting to attract attention, where the classes of testing nodes are unobserved in the training stage. Existing studies for ZNC mainly utilize Graph Neural Networks (GNNs) to construct the feature subspace to align with the classes’ semantic subspace, thus enabling knowledge transfer from seen classes to unseen classes. However, the modeling of the node feature is single-view and unilateral, e.g., the bag-of-words vector, which is not enough to fully describe the characteristics of the node itself. To address this dilemma, we propose to develop the Multi-View Enhanced zero-shot node classification paradigm (MVE) to promote the machine’s generality to approach the human-like thinking mode. Specifically, multi-view features are obtained from different aspects such as pre-trained model embeddings, knowledge graphs, statistic methods, and then fused by a contrastive learning module into the compositional node representation. Meanwhile, a developed Graph Convolutional Network (GCN) is used to make the nodes fully absorb the information of neighbors while the over-smooth issue is alleviated by multi-view features and the proposed contrastive learning mechanism. Experimental results conducted on three public datasets show an average 25% improvement compared to baseline methods, proving the superiority of our multi-view learning framework. The code and data can be found at https://github.com/guaiqihen/MVE. 相似文献
17.
《Journal of The Franklin Institute》2021,358(18):10165-10192
This paper develops a damage-risk assessing method with dynamic feature extraction optimization (DFEO) in the Thermal-wave image technique, to evaluate the hypervelocity impact (HVI) damages in dynamic meteoroid/orbital debris (M/OD) environment rapidly. The proposed DFEO not only investigates how to select effectively each representative temperature point (RTP) with consideration of similarities and differences of thermal characteristic data, but also studies how to utilize historical information to extract rapidly impact damage feature from thermal image sequence. A multi-directional prediction method (MPM) in DFEO calculates the degree of environment change to depict the configuration of previous Pareto set (PS) and predict the moving directions of the optimal solutions. Meanwhile, a dynamic multi-objective particle-swarm optimization strategy (DM-PSOS) in DFEO guarantees that the obtained initial population evolves successfully to Pareto Front (PF). Besides DFEO, the assessing method contains variable step-size search, clustering analysis and damage visualization, to enhance the detection efficiency. The experimental study on HVI damage detection demonstrates the capabilities of the proposed method. 相似文献
18.
In this paper, a document summarization framework for storytelling is proposed to extract essential sentences from a document by exploiting the mutual effects between terms, sentences and clusters. There are three phrases in the framework: document modeling, sentence clustering and sentence ranking. The story document is modeled by a weighted graph with vertexes that represent sentences of the document. The sentences are clustered into different groups to find the latent topics in the story. To alleviate the influence of unrelated sentences in clustering, an embedding process is employed to optimize the document model. The sentences are then ranked according to the mutual effect between terms, sentence as well as clusters, and high-ranked sentences are selected to comprise the summarization of the document. The experimental results on the Document Understanding Conference (DUC) data sets demonstrate the effectiveness of the proposed method in document summarization. The results also show that the embedding process for sentence clustering render the system more robust with respect to different cluster numbers. 相似文献
19.
《Information processing & management》2022,59(4):102949
This paper proposes a new method for semi-supervised clustering of data that only contains pairwise relational information. Specifically, our method simultaneously learns two similarity matrices in feature space and label space, in which similarity matrix in feature space learned by adopting adaptive neighbor strategy while another one obtained through tactful label propagation approach. Moreover, the above two learned matrices explore the local structure (i.e., learned from feature space) and global structure (i.e., learned from label space) of data respectively. Furthermore, most of the existing clustering methods do not fully consider the graph structure, they can not achieve the optimal clustering performance. Therefore, our method forcibly divides the data into c clusters by adding a low rank restriction on the graphical Laplacian matrix. Finally, a restriction of alignment between two similarity matrices is imposed and all items are combined into a unified framework, and an iterative optimization strategy is leveraged to solve the proposed model. Experiments in practical data show that our method has achieved brilliant performance compared with some other state-of-the-art methods. 相似文献
20.
《Journal of The Franklin Institute》2023,360(14):10706-10727
Distributed optimization over networked agents has emerged as an advanced paradigm to address large-scale control, optimization, and signal-processing problems. In the last few years, the distributed first-order gradient methods have witnessed significant progress and enrichment due to the simplicity of using only the first derivatives of local functions. An exact first-order algorithm is developed in this work for distributed optimization over general directed networks with only row-stochastic weighted matrices. It employs the rescaling gradient method to address unbalanced information diffusion among agents, where the weights on the received information can be arbitrarily assigned. Moreover, uncoordinated step-sizes are employed to magnify the autonomy of agents, and an error compensation term and a heavy-ball momentum are incorporated to accelerate convergency. A linear convergence rate is rigorously proven for strongly-convex objective functions with Lipschitz continuous gradients. Explicit upper bounds of step-size and momentum parameter are provided. Finally, simulations illustrate the performance of the proposed algorithm. 相似文献