检索结果-内蒙古大学图书馆

sparse non-negative matrix factorization for uncertain data clustering

INTELLIGENT DATA ANALYSIS 2022年第3期26卷 615-636页

作者： Chen, Danyang Wang, Xiangyu Xu, Xiu Zhong, Cheng Xu, Jinhui Guangxi Univ Sch Comp Elect & Informat Nanning Guangxi Peoples R China Cloud & Smart Ind Grp Tencent Guangdong Peoples R China China Univ Min & Technol Sch Comp Sci & Technol Xuzhou Jiangsu Peoples R China Univ Buffalo Dept Comp Sci & Engn Buffalo NY USA

We consider the problem of clustering a set of uncertain data, where each data consists of a point-set indicating its possible locations. The objective is to identify the representative for each uncertain data and group them into k clusters so as to minimize the total clustering cost. Different from other models, our model does not assume that there is a probability distribution for each uncertain data. Thus, all possible locations need to be considered to determine the representative. Existing methods for this problem are either impractical or have difficulty to handle large-scale datasets due to their pairwise-distance based global search strategy and expensive optimization computation. In this paper, we propose a novel sparse non-negative matrix factorization (NMF) method which measures the similarity of uncertain data by their most commonly shared features. A divide-and-conquer approach is adopted to remarkably improve the efficiency. A novel diagonal l(0)-constraint and its l(1) relaxation are proposed to overcome the challenge of determining the representatives. We give a detailed analysis to show the correctness of our method, and provide an effective initialization and peeling strategy to enhance the ability of processing large-scale datasets. Experimental results on some benchmark datasets confirm the effectiveness of our method.

关键词： Uncertain data clustering sparse non-negative matrix factorization data analysis machine learning

来源：评论

学校读者我要写书评

暂无评论

Convergence of proximal algorithms with stepsize controls for non-linear inverse problems and application to sparse non-negative matrix factorization

引用

NUMERICAL ALGORITHMS 2020年第4期85卷 1255-1279页

作者： Pham Quy Muoi Lachmund, Delf Hao Dinh Nho Univ Danang Univ Sci & Educ Dept Math 459 Ton Duc Thang Danang Vietnam Univ Bremen Ctr Ind Math Bibliothekstr 1 D-28359 Bremen Germany VAST Hanoi Inst Math 18 Hoang Quoc Viet Rd Hanoi Vietnam

We consider a general ill-posed inverse problem in a Hilbert space setting by minimizing a misfit functional coupling with a multi-penalty regularization for stabilization. For solving this minimization problem, we investigate two proximal algorithms with stepsize controls: a proximal fixed point algorithm and an alternating proximal algorithm. We prove the decrease of the objective functional and the convergence of both update schemes to a stationary point under mild conditions on the stepsizes. These algorithms are then applied to the sparse and non-negative matrix factorization problems. Based on a priori information of non-negativity and sparsity of the exact solution, the problem is regularized by corresponding terms. In both cases, the implementation of our proposed algorithms is straight-forward since the evaluation of the proximal operators in these problems can be done explicitly. Finally, we test the proposed algorithms for the non-negative sparse matrix factorization problem with both simulated and real-world data and discuss reconstruction performance, convergence, as well as achieved sparsity.

关键词： sparse matrix factorization sparse non-negative matrix factorization Proximal fixed point iteration Alternating proximal iteration non-smooth Optimization non-linear inverse problems Sparsity regularization non-negative sparsity regularization 49Mxx 65Kxx

来源：评论

学校读者我要写书评

暂无评论

Blind separation of fluorescence spectra using sparse non-negative matrix factorization on right hand factor

引用

JOURNAL OF CHEMOMETRICS 2015年第8期29卷 442-447页

作者： Yang, Ruifang Zhao, Nanjing Xiao, Xue Yu, Shaohui Liu, Jianguo Liu, Wenqing Chinese Acad Sci Anhui Inst Opt & Fine Mech Hefei 230031 Peoples R China Univ Sci & Technol China Hefei 230022 Peoples R China Hefei Normal Univ Sch Math & Stat Hefei 230061 Peoples R China

sparse non-negative matrix factorization on right side factor (SNMF/R) has better performance in feature extraction than non-negative matrix factorization. In this work, SNMF/R was first used to separate the overlapped three-dimensional fluorescence spectra of polycyclic aromatic hydrocarbons mixtures in pure water, lake water, and river water, respectively. It is found that the similarity coefficients between the acquired three-dimensional spectra and the corresponding reference spectra with random initials are all above 0.80;the recognition rate of SNMF/R is higher than that of PARAFAC and non-negative matrix factorization algorithms, especially in the case of lake water and river water samples. In addition, SNMF/R does not need any initialization scheme designing during spectra separation. These results demonstrate that SNMF/R is an appropriate algorithm to separate the overlapped fluorescence spectra of polycyclic aromatic hydrocarbons in aquatic environment accurately and effectively. Copyright (c) 2015 John Wiley & Sons, Ltd.

关键词： polycyclic aromatic hydrocarbons three-dimensional fluorescence spectra sparse non-negative matrix factorization component recognition

来源：评论

学校读者我要写书评

暂无评论

Probabilistic sparse non-negative matrix factorization 14th

Probabilistic Sparse Non-negative Matrix Factorization

引用

14th International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA)

作者： Hinrich, Jesper Love Morup, Morten Tech Univ Denmark Dept Appl Math & Comp Sci Richard Petersens Plads Bld 321 DK-2800 Lyngby Denmark

ISBN: (纸本)9783319937649;9783319937632

In this paper, we propose a probabilistic sparse non-negative matrix factorization model that extends a recently proposed variational Bayesian non-negative matrix factorization model to explicitly account for sparsity. We assess the influence of imposing sparsity within a probabilistic framework on either the loading matrix, score matrix, or both and further contrast the influence of imposing an exponential or truncated normal distribution as prior. The probabilistic methods are compared to conventional maximum likelihood based NMF and sparse NMF on three image datasets;(1) A (synthetic) swimmer dataset, (2) The CBCL face dataset, and (3) The MNIST handwritten digits dataset. We find that the probabilistic sparse NMF is able to automatically learn the level of sparsity and find that the existing probabilistic NMF as well as the proposed probabilistic sparse NMF prunes inactive components and thereby automatically learns a suitable number of components. We further find that accounting for sparsity can provide more part based representations but for the probabilistic modeling the choice of priors and how sparsity is imposed can have a strong influence on the extracted representations.

关键词： non-negative matrix factorization Sparsity Bayesian modeling sparse non-negative matrix factorization

来源：评论

学校读者我要写书评

暂无评论

An exploratory Q-matrix estimation method based on sparse non-negative matrix factorization

引用

BEHAVIOR RESEARCH METHODS 2024年第7期56卷 7647-7673页

作者： Xiong, Jianhua Luo, Zhaosheng Luo, Guanzhong Yu, Xiaofeng Li, Yujun Jiangxi Normal Univ Sch Psychol Nanchang Peoples R China Jiangxi Normal Univ Sch Comp & Informat Engn Nanchang Peoples R China

Cognitive diagnostic assessment (CDA) is widely used because it can provide refined diagnostic information. The Q-matrix is the basis of CDA, and can be specified by domain experts or by data-driven estimation methods based on observed response data. The data-driven Q-matrix estimation methods have become a research hotspot because of their objectivity, accuracy, and low calibration cost. However, most of the existing data-driven methods require known prior knowledge, such as initial Q-matrix, partial q-vector, or the number of attributes. Under the G-DINA model, we propose to estimate the number of attributes and Q-matrix elements simultaneously without any prior knowledge by the sparse non-negative matrix factorization (SNMF) method, which has the advantage of high scalability and universality. Simulation studies are carried out to investigate the performance of the SNMF. The results under a wide variety of simulation conditions indicate that the SNMF has good performance in the accuracy of attribute number and Q-matrix elements estimation. In addition, a set of real data is taken as an example to illustrate its application. Finally, we discuss the limitations of the current study and directions for future research.

关键词： Cognitive diagnostic assessment Q-matrix estimation Attribute number estimation sparse non-negative matrix factorization G-DINA model

来源：评论

学校读者我要写书评

暂无评论

Microarray Data Analysis of Yeast Data using sparse non-negative matrix factorization

Microarray Data Analysis of Yeast Data using Sparse Non-Nega...

引用

International Conference on Computational Science and Computational Intelligence (CSCI)

作者： Passi, Kalpdrum Draper, Paul Santala, Jillana Jain, Chakresh Kumar Laurentian Univ Dept Math & Comp Sci Sudbury ON Canada Jaypee Inst Informat Technol Dept Biotechnol Noida India

ISBN: (纸本)9781538626528

Microarray expression data contains observations from thousands of genes across hundreds of samples. To extract meaningful information from these large datasets, the dimensionality reduction technique known as non-negative matrix factorization, or NMF, is introduced. This tool transforms the data and makes it more amenable to clustering. NMF was applied to a yeast microarray dataset. Three main clusters were discovered, corresponding to three distinct metabolic cycles. The data were also clustered using the k-means algorithm, and the clustering result was highly similar to that obtained by NMF.

关键词： sparse non-negative matrix factorization yeast microarray data k-means

来源：评论

学校读者我要写书评

暂无评论

Encoding of rat working memory by power of multi-channel local field potentials via sparse non-negative matrix factorization

引用

Neuroscience Bulletin 2013年第3期29卷 279-286页

作者： Xu Liu Tiao-Tiao Liu Wen-Wen Bai Hu Yi Shuang-Yan Li Xin Tian School of Biomedical Engineering Tianjin Medical University Research Center of Basic Medicine Tianjin Medical University

Working memory plays an important role in human cognition. This study investigated how working memory was encoded by the power of multichannel local field potentials （LFPs） based on sparse non negative matrix factorization （SNMF）. SNMF was used to extract features from LFPs recorded from the prefrontal cortex of four SpragueDawley rats during a memory task in a Y maze, with 10 trials for each rat. Then the powerincreased LFP components were selected as working memoryrelated features and the other components were removed. After that, the inverse operation of SNMF was used to study the encoding of working memory in the time frequency domain. We demonstrated that theta and gamma power increased significantly during the working memory task. The results suggested that postsynaptic activity was simulated well by the sparse activity model. The theta and gamma bands were meaningful for encoding working memory.

关键词： sparse non-negative matrix factorization multi-channel local field potentials working memory prefrontal cortex

来源：评论

学校读者我要写书评

暂无评论

A Voice Activity Detection Algorithm Using sparse non-negative matrix factorization-based Model Learning in Spectro-Temporal Domain

引用

INTERNATIONAL JOURNAL OF ENGINEERING 2023年第8期36卷 1478-1488页

作者： Mavaddati, S. Univ Mazandaran Fac Engn & Technol Babolsar Iran

Voice activity detectors are presented to extract silence/speech segments of the speech signal to eliminate different background noise signals. A novel voice activity detector is proposed in this paper using spectro-temporal features extracted from the auditory model of the speech signal. After extracting the scale, rate, and frequency features from this feature space, a sparse structured principal component analysis algorithm is used to consider the basic components of these features and reduce the dimension of learning data. Then these feature vectors are employed to learn the models by the sparse non-negative matrix factorization algorithm. The model learning procedure is performed to represent each feature vector with a proper sparse rate based on the selected atoms. Voice activity detection of the input frames is performed by computing the energy of the sparse representation for each input frame over the composite model. If the calculated energy exceeds a specified threshold, it indicates that the input frame has a structure similar to the atoms of the learned models and concludes that the observed frame has voice content. The results of the proposed detector were compared with other baseline methods and classifiers in this processing field. These results in the presence of stationary, non-stationary and periodic noises were investigated and they are shown that the proposed method based on model learning with spectro-temporal features can correctly detect the silence/speech ***: 10.5829/ije.2023.36.08b.08

关键词： Voice Activity Detector Spectro-temporal Domain Spectro-temporal sparse Structured Principal Component Analysis sparse non-negative matrix factorization

来源：评论

学校读者我要写书评

暂无评论

sparse nonnegative matrix factorization for protein sequence motif discovery

引用

EXPERT SYSTEMS WITH APPLICATIONS 2011年第10期38卷 13198-13207页

作者： Kim, Wooyoung Chen, Bernard Kim, Jingu Pan, Yi Park, Haesun Georgia State Univ Dept Comp Sci Atlanta GA 30302 USA Univ Cent Arkansas Dept Comp Sci Conway AR 72035 USA Georgia Inst Technol Sch Computat Sci & Engn Atlanta GA 30332 USA

The problem of discovering motifs from protein sequences is a critical and challenging task in the field of bioinformatics. The task involves clustering relatively similar protein segments from a huge collection of protein sequences and culling high quality motifs from a set of clusters. A granular computing strategy combined with K-means clustering algorithm was previously proposed for the task, but this strategy requires a manual selection of biologically meaningful clusters which are to be used as an initial condition. This manipulated clustering method is undisciplined as well as computationally expensive. In this paper, we utilize sparse non-negative matrix factorization (SNMF) to cluster a large protein data set. We show how to combine this method with Fuzzy C-means algorithm and incorporate bio-statistics information to increase the number of clusters whose structural similarity is high. Our experimental results show that an SNMF approach provides better protein groupings in terms of similarities in secondary structures while maintaining similarities in protein primary sequences. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： sparse non-negative matrix factorization Clustering Protein sequence motif Fuzzy C-means Chou-Fasman parameters

来源：评论

学校读者我要写书评

暂无评论

non-negative matrix factorization With sparseness Constraints For Credit Risk Assessment

Non-Negative Matrix Factorization With Sparseness Constraint...

引用

IEEE International Conference on Grey Systems and Intelligent Services (GSIS)

作者： Liu, Yulong Du, Jianlei Wang, Feng Ohio State Univ Dept Math 231 West 18th Ave Columbus OH 43210 USA Univ Arizona Eller Business Coll Tucson AZ 85721 USA

ISBN: (纸本)9781467352475;9781467352482

As the most important tasks of a bank, assessment of credit card users is aimed to keep the risk of a credit loss low and to minimize costs of failure over risk groups. Credit risk assessment is an essential problem in finance. However, accessing credit risk is very difficult because many factors may contribute to the risk and their relationship is complicated to capture. Recent years have witnessed a growing trend in applying machine learning methods, such as SVM classifier, for credit risk analysis. SVM is a strong classifier that is effective in capturing nonlinear relationship in the data. However, high dimensional training data not only results in time-consuming computation but also affects the performance of the classifier. In this paper, we will adopt sparse non-negative matrix factorization to transform the data into lower dimensional space that will contribute to good performance in the credit risk classification. We test our method in a real-world credit risk prediction task, and our empirical results demonstrate the advantage of our method by comparing with other state of art methods.

关键词： Feature Extraction Machine Learning sparse non-negative matrix factorization L1-Norm Credit Risk Analysis

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：