检索结果-内蒙古大学图书馆

Incomplete multi-view clustering with incomplete graph-regularized orthogonal non-negative matrix factorization

APPLIED INTELLIGENCE 2022年第13期52卷 14607-14623页

作者： Liang, Naiyao Yang, Zuyuan Li, Zhenni Han, Wei Guangdong Univ Technol Sch Automat Guangdong Key Lab IoT Informat Technol Guangzhou 510006 Peoples R China Minist Educ Key Lab iDetect & Mfg IoT Guangzhou 510006 Peoples R China Guangzhou Railway Polytech Sch Elect Engn Guangzhou 510430 Guangdong Peoples R China

Incomplete multi-view clustering (IMC) has achieved widespread attention due to its advantage in fusing the multi-view information when the view samples are unobserved partly. Recently, it is shown that the clustering performance in the subspace can be improved by preserving the clustering structure of each view, but the problem of the inconsistent clustering structure caused by the incomplete graphs are seldom considered, restricting the clustering performance. Motivated by the clustering interpretation of the orthogonal non-negative matrix factorization, it is employed to unify the clustering structure of the data, and a new model called Incomplete Graph-regularized orthogonal non-negative matrix factorization (IGONMF) is proposed in this paper. In IGONMF, the reproduced representation is developed, based on which, a set of incomplete graphs are utilized to fully take advantage of the geometric structure of the data. And the orthogonality is further employed to alleviate the problem of the inconsistent clustering structure. Also, we design an effective iterative updating algorithm to solve the proposed model, along with its analysis on the convergence and the computational cost. Finally, experimental results on several real-world datasets indicate that our method is superior to the related state-of-the-art methods.

关键词： Clustering Incomplete multi-view clustering orthogonal non-negative matrix factorization Graph constraint

来源：评论

学校读者我要写书评

暂无评论

A competitive optimization approach for data clustering and orthogonal non-negative matrix factorization

引用

4OR-A QUARTERLY JOURNAL OF OPERATIONS RESEARCH 2021年第4期19卷 473-499页

作者： Dehghanpour-Sahron, Ja'far Mahdavi-Amiri, Nezam Sharif Univ Technol Fac Math Sci POB 11155-9415 Tehran Iran

Partitioning a given data-set into subsets based on similarity among the data is called clustering. Clustering is a major task in data mining and machine learning having many applications such as text retrieval, pattern recognition, and web mining. Here, we briefly review some clustering related problems (k-means, normalized k-cut, orthogonal non-negative matrix factorization, ONMF, and isoperimetry) and describe their connections. We formulate the relaxed mean version of the isoperimetry problem as an optimization problem with non-negative orthogonal constraints. We first make use of a gradient-based optimization algorithm to solve this kind of a problem, and then apply a post-processing technique to extract a solution of the clustering problem. Also, we propose a simplified approach to improve upon solution of the 2-dimensional clustering problem, using the N-nearest neighbor graph. Inspired by this technique, we apply a multilevel method for clustering a given data-set to reduce the size of the problem by grouping a number of similar vertices. The number is determined based on two values, namely, the maximum and the average of the edge weights of the vertices connected to a selected vertex. In addition, using the connections between ONMF and k-means and between k-means and the isoperimetry problem, we propose an algorithm to solve the ONMF problem. A comparative performance analysis of our approach with other related methods shows outperformance of our approach, in terms of the obtained misclassification error rate and Rand index, on both benchmark and randomly generated problems as well as hard synthetic data-sets.

关键词： Clustering Multilevel method Normalized k-cut Optimization problem orthogonal non-negative matrix factorization

来源：评论

学校读者我要写书评

暂无评论

A HOMOTOPY OPTIMIZATION METHOD FOR orthogonal non-negative matrix factorization 29

A HOMOTOPY OPTIMIZATION METHOD FOR ORTHOGONAL NON-NEGATIVE M...

引用

29th European Signal Processing Conference (EUSIPCO)

作者： Liu, Ya Shao, Mingjie Ma, Wing-Kin Chinese Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China

ISBN: (纸本)9789082797060

Data clustering is a key problem in data science and machine learning. In this paper, we consider orthogonal non-negative matrix factorization (ONMF) for scaled data clustering. The non-convex orthogonality constraint of ONMF raises a great challenge from an optimization viewpoint. We study a convex-constrained transformation of ONMF that allows us to control the approximation accuracy and problem difficulty through a parameter. We then apply a homotopy strategy in which we trace the solution path of a sequence of the aforementioned transformed problems, gradually moving from easy problems to near-ONMF problems. Intuitively, doing so may allow us to avoid local minima. Numerical results show that our homotopy method yields competitive clustering performance in synthetic data experiments and in a real-data hyperspectral clustering experiment.

关键词： clustering orthogonal non-negative matrix factorization homotopy optimization

来源：评论

学校读者我要写书评

暂无评论

Detection of valvular heart diseases combining orthogonal non-negative matrix factorization and convolutional neural networks in PCG signals

引用

JOURNAL OF BIOMEDICAL INFORMATICS 2023年第1期145卷 104475页

作者： Torre-Cruz, J. Canadas-Quesada, F. Ruiz-Reyes, N. Vera-Candeas, P. Garcia-Galan, S. Carabias-Orti, J. Ranilla, J. Univ Jaen Dept Telecommun Engn Campus Cientif Tecnol LinaresAvda UnivS-N Linares 23700 Jaen Spain Univ Oviedo Dept Comp Sci Campus Gijon S-N Gijon 33203 Asturias Spain

Background and objective: Valvular heart disease (VHD) is associated with elevated mortality rates. Although transthoracic echocardiography (TTE) is the gold standard detection tool, phonocardiography (PCG) could be an alternative as it is a cost-effective and noninvasive method for cardiac auscultation. Many researchers have dedicated their efforts to improving the decision-making process and developing robust and precise approaches to assist physicians in providing reliable diagnoses of ***: This research proposes a novel approach for the detection of anomalous valvular heart sounds from PCG signals. The proposed approach combines orthogonal non-negative matrix factorization (ONMF) and convolutional neural network (CNN) architectures in a three-stage cascade. The aim of the proposal is to improve the learning process by identifying the optimal ONMF temporal or spectral patterns for accurate detection. In the first stage, the time-frequency representation of the input PCG signal is computed. Next, band-pass filtering is performed to locate the spectral range that is most relevant for the presence of such cardiac abnormalities. In the second stage, the temporal and spectral cardiac structures are extracted using the ONMF approach. These structures are utilized in the third stage and fed into the CNN architecture to detect abnormal heart ***: Several state-of-the-art CNN architectures, such as LeNet5, AlexNet, ResNet50, VGG16 and GoogLeNet, have been evaluated to determine the effectiveness of using ONMF temporal features for VHD detection. The results reveal that the integration of ONMF temporal features with a CNN classifier significantly improve VHD detection. Specifically, the proposed approach achieves an accuracy improvement of approximately 45% when ONMF spectral features are used and 35% when time-frequency features from the short-time Fourier transform (STFT) spectrogram are used. Additionally, feeding ONMF temporal features into low-compl

关键词： Phonocardiogram Abnormal heart sounds orthogonal non-negative matrix factorization Convolutional neural network Spectral pattern Valvular heart sound

来源：评论

学校读者我要写书评

暂无评论

Co-consensus semi-supervised multi-view learning with orthogonal non-negative matrix factorization

引用

INFORMATION PROCESSING & MANAGEMENT 2022年第5期59卷

作者： Liang, Naiyao Yang, Zuyuan Li, Zhenni Xie, Shengli Guangdong Univ Technol Guangdong Key Lab IoT Informat Technol Sch Automat Guangzhou 510006 Peoples R China Minist Educ Key Lab iDetect & Mfg IoT Guangzhou 510006 Peoples R China Guangdong HongKong Macao Joint Lab Smart Discrete Guangzhou 510006 Peoples R China

Semi-supervised multi-view learning has recently achieved appealing performance with the consensus relation between samples. However, in addition to the relation between samples, the relation between samples and their assemble centroid is also important to the learning. In this paper, we propose a novel model based on orthogonal non-negative matrix factorization, which allows exploring both the consensus relations between samples and between samples and their assemble centroid. Since this model utilizes more consensus information to guide the multi-view learning, it can lead to better performance. Meanwhile, we theoretically derive a proposition about the equivalency between the partial orthogonality and the full orthogonality. Based on this proposition, the orthogonality constraint and the label constraint are simultaneously implemented in the proposed model. Experimental evaluations on five real-world datasets show that our approach outperforms the state-of-the-art methods, where the improvement is 6% average in terms of ARI index.

关键词： Multi-view learning Semi-supervised learning orthogonal constraint orthogonal non-negative matrix factorization Consensus

来源：评论

学校读者我要写书评

暂无评论

Applying Time Series for Background User Identification Based on Their Text Data Analysis

引用

PROGRAMMING AND COMPUTER SOFTWARE 2018年第5期44卷 353-362页

作者： Korolev, V. Yu Korehagin, A. Yu Mashechkin, I., V Petrovskii, M., I Tsarev, D., V Lomonosov State Univ Fac Computat Math & Cybernet Moscow 119991 Russia

An approach to user identification based on deviations of their topic trends in operation with text information is presented. An approach is proposed to solve this problem;the approach implies topic analysis of the user's past trends (behavior) in operation with text content of various (including confidential) categories and forecast of their future behavior. The topic analysis of user's operation implies determining the principal topics of their text content and calculating their respective weights at the given instants. Deviations in the behavior in the user's operation with the content from the forecast are used to identify this user. In the framework of this approach, our own original time series forecasting method is proposed based on orthogonal non-negative matrix factorization (ONMF). Note that ONMF has not been used to solve time series forecasting problems before. The experimental research held on the example of real-world corporate emailing formed out of the Enron data set showed the proposed user identification approach to be applicable.

关键词： computer security user identification topic modelling orthogonal non-negative matrix factorization time series forecasting

来源：评论

学校读者我要写书评

暂无评论

Clustering by orthogonal NMF Model and non-Convex Penalty Optimization

引用

IEEE TRANSACTIONS ON SIGNAL PROCESSING 2021年 69卷 5273-5288页

作者： Wang, Shuai Chang, Tsung-Hui Cui, Ying Pang, Jong-Shi Chinese Univ Hong Kong Shenzhen Res Inst Big Data Shenzhen 518172 Peoples R China Chinese Univ Hong Kong Sch Sci & Engn Shenzhen 518172 Peoples R China Chinese Univ Hong Kong Guangdong Prov Key Lab Big Data Comp Shenzhen 518172 Peoples R China Univ Minnesota Dept Ind & Syst Engn Minneapolis MN 55455 USA Univ Southern Calif Dept Ind & Syst Engn Los Angeles CA 90089 USA

The non-negative matrix factorization (NMF) model with an additional orthogonality constraint on one of the factor matrices, called the orthogonal NMF (ONMF), has been found a promising clustering model and can outperform the classical K-means. However, solving the ONMF model is a challenging optimization problem because the coupling of the orthogonality and non-negativity constraints introduces a mixed combinatorial aspect into the problem due to the determination of the correct status of the variables (positive or zero). Most of the existing methods directly deal with the orthogonality constraint in its original form via various optimization techniques, but are not scalable for large-scale problems. In this paper, we propose a new ONMF based clustering formulation that equivalently transforms the orthogonality constraint into a set of norm-based non-convex equality constraints. We then apply a non-convex penalty (NCP) approach to add them to the objective as penalty terms, leading to a problem that is efficiently solvable. One smooth penalty formulation and one non-smooth penalty formulation are respectively studied. We build theoretical conditions for the penalized problems to provide feasible stationary solutions to the ONMF based clustering problem, as well as proposing efficient algorithms for solving the penalized problems of the two NCP methods. Experimental results based on both synthetic and real datasets are presented to show that the proposed NCP methods are computationally time efficient, and either match or outperform the existing K-means and ONMF based methods in terms of the clustering performance.

关键词： Dimensionality reduction Couplings Current measurement Clustering algorithms Signal processing algorithms Transforms Euclidean distance Data clustering orthogonal non-negative matrix factorization non-convex penalty method

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：