检索结果-内蒙古大学图书馆

Two-dimensional semi-nonnegative matrix factorization for clustering

INFORMATION SCIENCES 2022年 590卷 106-141页

作者： Peng, Chong Zhang, Zhilu Chen, Chenglizhao Kang, Zhao Cheng, Qiang Qingdao Univ Coll Comp Sci & Technol Qingdao Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu Peoples R China Univ Kentucky Dept Comp Sci Lexington KY 40506 USA Univ Kentucky Inst Biomed Informat Lexington KY 40506 USA

In this paper, we propose a new semi-nonnegative matrix factorization method for 2-dimensional (2D) data, named TS-NMF. It overcomes the drawback of existing methods that seriously damage the spatial information of the data by converting 2D data to vectors in a preprocessing step. In particular, projection matrices are sought under the guidance of building new data representations, such that the spatial information is retained and projections are enhanced by the goal of clustering, which helps construct optimal projection directions. Moreover, to exploit nonlinear structures of the data, manifold is constructed in the projected subspace, which is adaptively updated according to the projections and less afflicted with noise and outliers of the data and thus more representative in the projected space. Hence, seeking projections, building new data representations, and learning manifold are seamlessly integrated in a single model, which mutually enhance other and lead to a powerful data representation. Comprehensive experimental results verify the effectiveness of TS-NMF in comparison with several state-of-the-art algorithms, which suggests high potential of the proposed method for real world applications. (C) 2022 Elsevier Inc. All rights reserved.

关键词： semi-nonnegative matrix factorization Two-dimensional Clustering

来源：评论

学校读者我要写书评

暂无评论

Multi-View Image Clustering via Representations Fusion Method With semi-nonnegative matrix factorization

引用

IEEE ACCESS 2021年 9卷 96233-96243页

作者： Li, Guopeng Han, Kun Pan, Zhisong Wang, Shuaihui Song, Dan Natl Univ Def Technol Coll Informat & Commun Xian 710106 Peoples R China Army Engn Univ PLA Command & Control Engn Coll Nanjing 210007 Peoples R China

Multi-view clustering aims to obtain the perfect clusters with a set of feature sets. Many methods learn a common agreement among views to achieve this. However, they may fail to use the specific unique feature of each view, which is important to describe the intrinsic characteristics of data. Unlike many works, we treat each view as a subset feature of data and fuse representations from those unique views to learn an integrated graph for clustering. We propose a novel representations fusion method for MVC. In this method, a regularized semi-nonnegative matrix factorization is proposed to learn the low-dimensional representation of each view, in which, a regularization term is designed to keep the neighboring structure in new low-dimensional space, and it is further updated and utilized to guide the learning process. Then the learned representations is fused to get an integrated graph for describing the image data. Finally, extensive experiments on several real image datasets show that our method achieves better performance than the state-of-the-art methods.

关键词： Fuses Clustering methods Licenses Kernel Heating systems Data mining Visualization Multi-view clustering semi-nonnegative matrix factorization image representation neighboring regularization

来源：评论

学校读者我要写书评

暂无评论

The application of semi-nonnegative matrix factorization for detection of incipient faults in bearings

引用

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2019年第13期233卷 4543-4555页

作者： Rai, Akhand Upadhyay, Sanjay H. Indian Inst Technol Roorkee Dept Mech & Ind Engn Roorkee 247667 Uttarakhand India

Bearing faults are a major reason for the catastrophic breakdown of rotating machinery. Therefore, the early detection of bearing faults becomes a necessity to attain an uninterrupted and safe operation. This paper proposes a novel approach based on semi-nonnegative matrix factorization for detection of incipient faults in bearings. The semi-nonnegative matrix factorization algorithm creates a sparse, localized, part-based representation of the original data and assists to capture the fault information in bearing signals more effectively. Through semi-nonnegative matrix factorization, two bearing health indicators are derived to fulfill the desired purpose. In doing so, the paper tries to address two critical issues: (i) how to reduce the dimensionality of feature space (ii) how to obtain a definite range of the indicator between 0 and 1. Firstly, a set of time domain, frequency domain, and time-frequency domain features are extracted from the bearing vibration signals. Secondly, the feature dataset is utilized to train the semi-nonnegative matrix factorization algorithm which decomposes the training data matrix into two new matrices of lower ranks. Thirdly, the test feature vectors are projected onto these lower dimensional matrices to obtain two statistics called as square prediction error and Q(2). Finally, the Bayesian inference approach is exploited to convert the two statistics into health indicators that have a fixed range between [0-1]. The application of the advocated technique on experimental bearing signals demonstrates that it can effectively predict the weak defects in bearings as well as performs better than the earlier methods like principal component analysis and locality preserving projections.

关键词： Bearings fault detection health index semi-nonnegative matrix factorization Bayesian inference probability

来源：评论

学校读者我要写书评

暂无评论

Detecting drug communities and predicting comprehensive drug-drug interactions via balance regularized semi-nonnegative matrix factorization

引用

JOURNAL OF CHEMINFORMATICS 2019年第1期11卷 28-28页

作者： Shi, Jian-Yu Mao, Kui-Tao Yu, Hui Yiu, Siu-Ming Northwestern Polytech Univ Sch Life Sci Xian Shaanxi Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian Shaanxi Peoples R China Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

Background: Because drug-drug interactions (DDIs) may cause adverse drug reactions or contribute to complex-disease treatments, it is important to identify DDIs before multiple-drug medications are prescribed. As the alternative of high-cost experimental identifications, computational approaches provide a much cheaper screening for potential DDIs on a large scale manner. Nevertheless, most of them only predict whether or not one drug interacts with another, but neglect their enhancive (positive) and depressive (negative) changes of pharmacological effects. Moreover, these comprehensive DDIs do not occur at random, but exhibit a weakly balanced relationship (a structural property when considering the DDI network), which would help understand how high-order DDIs work. Results: This work exploits the intrinsically structural relationship to solve two tasks, including drug community detection as well as comprehensive DDI prediction in the cold-start scenario. Accordingly, we first design a balance regularized semi-nonnegative matrix factorization (BRSNMF) to partition the drugs into communities. Then, to predict enhancive and degressive DDIs in the cold-start scenario, we develop a BRSNMF-based predictive approach, which technically leverages drug-binding proteins (DBP) as features to associate new drugs (having no known DDI) with other drugs (having known DDIs). Our experiments demonstrate that BRSNMF can generate the drug communities, which exhibit more reasonable sizes, the property of weak balance as well as pharmacological significances. Moreover, they demonstrate the superiority of DBP features and the inspiring ability of the BRSNMF-based predictive approach on comprehensive DDI prediction with 94% accuracy among top-50 predicted enhancive and 86% accuracy among bottom-50 predicted degressive DDIs. Conclusions: Owing to the regularization of the weak balance property of the comprehensive DDI network into semi-nonnegative matrix factorization, our proposed BRSNMF

关键词： Drug-drug interaction Weak balance theory semi-nonnegative matrix factorization Regularization Community

来源：评论

学校读者我要写书评

暂无评论

Discovering Dynamic Patterns of Urban Space via semi-nonnegative matrix factorization 5

Discovering Dynamic Patterns of Urban Space via Semi-Nonnega...

引用

IEEE International Conference on Big Data (IEEE Big Data)

作者： Liu, Zhicheng Cao, Jun Yang, Junyan Wang, Qiao Southeast Univ Sch Informat Sci & Engn Nanjing Jiangsu Peoples R China Southeast Univ Sch Architecture Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781538627150

Dynamic patterns of urban space reveals the interaction of human activities and urban space, and draws much attention from urban planners and decision makers. The most common way to extract dynamic patterns from city-wide level human dynamics is through Singular Value Decomposition (SVD). However, SVD constrains the columns of matrix factors to be orthonormal. Some of the entries of matrix factors must be negative which leads to difficulties in interpretation of the decomposed components when each component is physically required to be nonnegative, e.g. population density. Therefore, we propose using semi-nonnegative matrix factorization (semi-NMF) framework to extract dynamic patterns of city-wide level population density. semi-NMF constrains dynamic patterns of population density to be nonnegative which results in parts-based representation and interpretable patterns. We apply our approach to a mobile phone dataset of Nanjing and extract interpretable dynamic patterns. Based on these patterns, we present application in urban planning to investigate commuter towns in Nanjing.

关键词： semi-nonnegative matrix factorization Human Mobility Mobile Phone

来源：评论

学校读者我要写书评

暂无评论

EXACT AND HEURISTIC ALGORITHMS FOR semi-nonnegative matrix factorization

引用

SIAM JOURNAL ON matrix ANALYSIS AND APPLICATIONS 2015年第4期36卷 1404-1424页

作者： Gillis, Nicolas Kumar, Abhishek Univ Mons Fac Polytech Dept Math & Operat Res B-7000 Mons Belgium IBM Corp Thomas J Watson Res Ctr Yorktown Hts NY 10598 USA

Given a matrix M (not necessarily nonnegative) and a factorization rank r, semi-nonnegative matrix factorization (semi-NMF) looks for a matrix U with r columns and a nonnegative matrix V with r rows such that UV is the best possible approximation of M according to some metric. In this paper, we study the properties of semi-NMF from which we develop exact and heuristic algorithms. Our contribution is threefold. First, we prove that the error of a semi-NMF of rank r has to be smaller than the best unconstrained approximation of rank r - 1. This leads us to a new initialization procedure based on the singular value decomposition (SVD) with a guarantee on the quality of the approximation. Second, we propose an exact algorithm (that is, an algorithm that finds an optimal solution), also based on the SVD, for a certain class of matrices (including nonnegative irreducible matrices) from which we derive an initialization for matrices not belonging to that class. Numerical experiments illustrate that this second approach performs extremely well, and allows us to compute optimal semi-NMF decompositions in many situations. Finally, we analyze the computational complexity of semi-NMF proving its NP-hardness, already in the rank-one case (that is, for r = 1), and we show that semi-NMF is sometimes ill-posed (that is, an optimal solution does not exist).

关键词： low-rank matrix approximation semi-nonnegative matrix factorization semi-nonnegative rank initialization algorithms

来源：评论

学校读者我要写书评

暂无评论

Structure constrained semi-nonnegative matrix factorization for EEG-based motor imagery classification

引用

COMPUTERS IN BIOLOGY AND MEDICINE 2015年 60卷 32-39页

作者： Lu, Na Li, Tengfei Pan, Linjin Ren, Xiaodong Peng, Zuren Miao, Hongyu Xi An Jiao Tong Univ Syst Engn Inst State Key Lab Mfg Syst Engn Xian 710049 Shaanxi Peoples R China Univ Rochester Sch Med & Dent Dept Biostat & Computat Biol Rochester NY USA

Background: Electroencephalogram (EEG) provides a non-invasive approach to measure the electrical activities of brain neurons and has long been employed for the development of brain-computer interface (BCI). For this purpose, various patterns/features of EEG data need to be extracted and associated with specific events like cue-paced motor imagery. However, this is a challenging task since EEG data are usually non-stationary time series with a low signal-to-noise ratio. New method: In this study, we propose a novel method, called structure constrained semi-nonnegative matrix factorization (SCS-NMF), to extract the key patterns of EEG data in time domain by imposing the mean envelopes of event-related potentials (ERPs) as constraints on the semi-NMF procedure. The proposed method is applicable to general EEG time series, and the extracted temporal features by SCS-NMF can also be combined with other features in frequency domain to improve the performance of motor imagery classification. Results: Real data experiments have been performed using the SCS-NMF approach for motor imagery classification, and the results clearly suggest the superiority of the proposed method. Comparison with existing methods: Comparison experiments have also been conducted. The compared methods include ICA, PCA, semi-NMF, Wavelets, EMD and CSP, which further verified the effectivity of SCS-NMF. Conclusions: The SCS-NMF method could obtain better or competitive performance over the state of the art methods, which provides a novel solution for brain pattern analysis from the perspective of structure constraint. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： EEG Brain computer interface Motor imagery classification Event-related potential Structure constraint semi-nonnegative matrix factorization

来源：评论

学校读者我要写书评

暂无评论

Nonlinear Unmixing of Hyperspectral Data Using semi-nonnegative matrix factorization

引用

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2014年第2期52卷 1430-1437页

作者： Yokoya, Naoto Chanussot, Jocelyn Iwasaki, Akira Univ Tokyo Dept Aeronaut & Astronaut Tokyo 1530041 Japan Grenoble Inst Technol F-38402 St Martin Dheres France

Nonlinear spectral mixture models have recently received particular attention in hyperspectral image processing. In this paper, we present a novel optimization method of nonlinear unmixing based on a generalized bilinear model (GBM), which considers the second-order scattering of photons in a spectral mixture model. semi-nonnegative matrix factorization (semi-NMF) is used for the optimization to process a whole image in matrix form. When endmember spectra are given, the optimization of abundance and interaction abundance fractions converge to a local optimum by alternating update rules with simple implementation. The proposed method is evaluated using synthetic datasets considering its robustness for the accuracy of endmember extraction and spectral complexity, and shows smaller errors in abundance fractions rather than conventional methods. GBM-based unmixing using semi-NMF is applied to the analysis of an airborne hyperspectral image taken over an agricultural field with many endmembers, and it visualizes the impact of a nonlinear interaction on abundance maps at reasonable computational cost.

关键词： Generalized bilinear model (GBM) nonlinear unmixing semi-nonnegative matrix factorization

来源：评论

学校读者我要写书评

暂无评论

DNSRF: Deep Network-based semi-NMF Representation Framework

引用

ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY 2024年第5期15卷 1-20页

作者： Wang, Dexian Li, Tianrui Deng, Ping Luo, Zhipeng Zhang, Pengfei Liu, Keyu Huang, Wei Southwest Jiaotong Univ Chengdu Univ Tradit Chinese Med Chengdu Peoples R China Southwest Jiaotong Univ Engn Res Ctr Sustainable Urban Intelligent Transpo Minist Educ Chengdu Peoples R China Xihua Univ Chengdu Peoples R China Southwest Jiaotong Univ Chengdu Peoples R China Chengdu Univ Tradit Chinese Med Chengdu Peoples R China

Representation learning is an important topic in machine learning, pattern recognition, and data mining research. Among many representation learning approaches, semi-nonnegative matrix factorization (SNMF) is a frequently-used one. However, a typical problem of SNMF is that usually there is no learning rate guidance during the optimization process, which often leads to a poor representation ability. To overcome this limitation, we propose a very general representation learning framework (DNSRF) that is based on a deep neural net. Essentially, the parameters of the deep net used to construct the DNSRF algorithms are obtained by matrix element update. In combination with different activation functions, DNSRF can be implemented in various ways. In our experiments, we tested nine instances of our DNSRF framework on six benchmark datasets. In comparison with other state-of-the-art methods, the results demonstrate the superior performance of our framework, which is thus shown to have a great representation ability.

关键词： Representation learning semi-nonnegative matrix factorization deep network clustering

来源：评论

学校读者我要写书评

暂无评论

A multi-view clustering algorithm based on deep semi-NMF

引用

INFORMATION FUSION 2023年第1期99卷

作者： Wang, Dexian Li, Tianrui Huang, Wei Luo, Zhipeng Deng, Ping Zhang, Pengfei Ma, Minbo Southwest Jiaotong Univ Sch Comp & Artificial Intelligence Chengdu 611756 Peoples R China Southwest Jiaotong Univ Natl Engn Lab Integrated Transportat Big Data Appl Chengdu 611756 Peoples R China Mfg Ind Chains Collaborat & Informat Support Techn Chengdu 611756 Peoples R China Minist Educ Engn Res Ctr Sustainable Urban Intelligent Transpo Beijing Peoples R China Xihua Univ Sch Comp Sci & Software Engn Chengdu 610039 Peoples R China

Multi-view clustering (MVC) aims to fuse the information among multiple views to achieve effective clustering. Many MVC algorithms based on semi-nonnegative matrix factorization (SNMF) typically have two issues: (1) their optimization schemes are not flexible enough;and (2) the variables are updated only rely on the data but not guided by learning rate. These problems can result in very poor clusters generated. In this paper, we present a multi-view clustering algorithm based on deep SNMF (MCDS) to resolve these issues. Specifically, we first design two types of activation functions to restrict the value domain of the element in the low-dimensional matrix to eliminate the constraint. Then, the SGD algorithm is used to implement element update guided by the learning rate. After obtaining the corresponding weight matrix and bias matrix, we combine them with the activation functions to construct a deep SNMF (DSNMF) network. This network is to update the element in the corresponding low-dimensional matrix for each view and obtain the consensus matrix. To validate the proposed algorithm, numerous experiments are performed on six multi-view datasets including both normal and large-scale datasets. The results demonstrate that MCDS can achieve excellent clustering results and outperform other competitive methods.

关键词： Multi-view fusion semi-nonnegative matrix factorization Multi-view clustering Deep learning

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：