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检索条件"主题词=Nonnegative Matrix factorization"

共 1478 条记录，以下是261-270 订阅

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ACCELERATING nonnegative matrix factorization OVER POLYNOMIAL SIGNALS WITH FASTER PROJECTIONS 29

ACCELERATING NONNEGATIVE MATRIX FACTORIZATION OVER POLYNOMIA...

引用

IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP)

作者： Hautecoeur, Cecile Glineur, Francois Catholic Univ Louvain CORE B-1348 Louvain La Neuve Belgium Catholic Univ Louvain ICTEAM Inst B-1348 Louvain La Neuve Belgium

ISBN: (纸本)9781728108247

nonnegative matrix factorization is a data analysis tool that aims at representing a set of input data vectors as nonnegative linear combinations of a few nonnegative basis vectors. When dealing with continuous input signals, smoothness and accuracy of this representation can often be improved if nonnegative polynomials are used in the basis instead of vectors. However, algorithms using polynomials are usually more computationally demanding than their vector counterparts. In this work, we consider the Hierarchical Alternating Least Squares method, which displays state-of-the art performance on this problem and requires at each iteration to compute projections over the set of nonnegative polynomials. We introduce several heuristic algorithms designed to provide fast approximations of these projections, and show that their use significantly accelerates the resolution of nonnegative matrix factorization problems over polynomial signals without adverse effect on the accuracy of the obtained solutions.

关键词： nonnegative matrix factorization polynomial signals nonnegative polynomials hierarchical alternating least squares projection over nonnegative polynomials

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Regularized nonnegative matrix factorization with Real Data for Hyperspectral Unmixing 4

Regularized Nonnegative Matrix Factorization with Real Data ...

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4th IEEE International Conference on Signal and Image Processing (ICSIP)

作者： Sun, Li Feng, Wei Wang, Jing Shandong Agr Univ Coll Informat Sci & Engn Tai An Shandong Peoples R China

ISBN: (纸本)9781728136608

nonnegative matrix factorization has been applied in hyperspectral unmixing, while the accuracy of unmixing is closely related with the local minimizers In this paper, we present a new regularized cost function of nonnegative matrix factorization by fully considering the real data information of the endmember signature. The endmember signatures can be easily found in the United States Geological Survey spectral library. The multiplicative update rules are employed to obtain the factor matrices, because it is easy to implement and often yields good results. We demonstrate the success of regularized nonnegative matrix factorization by applying it on hyperspectral unmixing.

关键词： nonnegative matrix factorization multiplicative update rules hyperspectral unmixing regularized term real data

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GRADIENT-BASED JOINT-VARIABLES nonnegative matrix factorization FOR MULTI-SHARPENING HYPERSPECTRAL REMOTE SENSING DATA 39

GRADIENT-BASED JOINT-VARIABLES NONNEGATIVE MATRIX FACTORIZAT...

引用

IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Karoui, Moussa Sofiane Benhalouche, Fatima Zohra Boukerch, Issam Ctr Tech Spatiales Arzew Algeria

ISBN: (纸本)9781538691540

This paper presents a fusion method generating unobservable sharpened hyperspectral remote sensing data with high spatial and spectral resolutions. This method, related to linear spectral unmixing (LSU) techniques, and based on nonnegative matrix factorization (NMF), introduces Joint-Variables NMF (JVNMF) for fusing observable low spatial resolution hyperspectral and high spatial resolution multispectral data. It optimizes a joint-variables criterion that exploits spatial and spectral degradation models between the two considered images, and therefore considers a reduced number of unknown variables. This approach, called Grd-JVNMF, is a gradient-based method and uses iterative update rules. The proposed method is applied to realistic synthetic and semi-real data, and its effectiveness, in spatial and spectral domains, is evaluated with established performance criteria. Experimental results show that the proposed Grd-JVNMF method yields multi-sharpened hyperspectral data with good spectral and spatial fidelities. These tests also show that the proposed method outperforms tested literature ones.

关键词： Hyper/multispectral imaging data fusion multi-sharpening linear spectral unmixing nonnegative matrix factorization spatial-spectral degradation models

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FASTMNMF: JOINT DIAGONALIZATION BASED ACCELERATED ALGORITHMS FOR MULTICHANNEL nonnegative matrix factorization 44

FASTMNMF: JOINT DIAGONALIZATION BASED ACCELERATED ALGORITHMS...

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44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Ito, Nobutaka Nakatani, Tomohiro NTT Corp NTT Commun Sci Labs Kyoto Japan

ISBN: (纸本)9781479981311

A multichannel extension of nonnegative matrix factorization (NMF) for audio/music data, called multichannel NMF (MNMF), has been proposed by Sawada et al. ["Multichannel extensions of non-negative matrix factorization with complex-valued data," IEEE Trans. ASLP, vol. 21, no. 5, pp. 971-982, May 2013]. However, conventional MNMF algorithms have a major drawback of a heavy computational load due to numerous matrix operations, such as matrix inversions and matrix multiplications. Here we propose FastMNMF, accelerated algorithms for the MNMF based on joint diagonalization of matrices. It is well known that, for diagonal matrices, matrix operations reduce to mere scalar operations on diagonal entries. Because of this property, the joint diagonalization results in a significantly reduced computational load compared to conventional MNMF algorithms. This makes the proposed FastMNMF even applicable to a situation with a large database or restricted computational resources.

关键词： nonnegative matrix factorization joint diagonalization source separation microphone arrays

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CONSTRAINED nonnegative matrix factorization FOR HYPERSPECTRAL CHANGE DETECTION 39

CONSTRAINED NONNEGATIVE MATRIX FACTORIZATION FOR HYPERSPECTR...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Erturk, Alp Kocaeli Univ Lab Image & Signal Proc Izmit Turkey

ISBN: (纸本)9781538691540

This paper presents an unmixing based change detection (UBCD) approach based on constrained nonnegative matrix factorization (NMF) for hyperspectral images. UBCD provides not only multi -output change detection, but also subpixel level information about the nature of the changes that occur in the scene. The proposed method utilizes constrained NMF with the sparsity constraint for the abundances and the minimum volume constraint for the endmembers, reducing the solution space for the matrix factorization and resulting in enhanced unmixing and change detection performance. The change detection output is obtained in terms of the temporal abundance matrix differences for each endmember. The proposed method is evaluated on synthetic and real multitemporal datasets.

关键词： Change detection hyperspectral multitemporal nonnegative matrix factorization unmixing

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A Novel nonnegative matrix factorization Method for Hyperspectral Unmixing 10

A Novel Nonnegative Matrix Factorization Method for Hyperspe...

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10th International Conference on Graphics and Image Processing (ICGIP)

作者： Xu, Nan Yang, Huadong Shenyang Jianzhu Univ BIM Comp & Res Ctr Shenyang 110168 Liaoning Peoples R China Shenyang Ligong Univ Sch Informat Sci & Engn Shenyang 110159 Liaoning Peoples R China

ISBN: (数字)9781510628298

ISBN: (纸本)9781510628298

In this paper, we propose a new algorithm integrating pure pixel identification into nonnegative matrix factorization (NMF) model to decompose the mixed pixels existing in hyperspectral imagery. The proposed algorithm employs traditional endmember identification algorithm to search for the pure pixel candidates, and then the principal component analysis is performed on the homogenous pixels which consist of the pure pixel candidates and its neighborhoods to identify the endmembers existing in the real scene. Finally, the known-endmember-based NMF unmixing algorithm is used to generate the other unknown endmembers. The proposed algorithm retains the advantages of both pure pixel identification method and NMF. Experimental results based on simulated and real data sets demonstrate the superiority of the proposed algorithm with respect to other state-of-the-art approaches.

关键词： Hyperspectral imagery hyperspectral unmixing endmember extraction principal component analysis nonnegative matrix factorization

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nonnegative matrix factorization for text, graph, and hybrid data analytics

Nonnegative matrix factorization for text, graph, and hybrid...

引用

作者： Du, Rundong Georgia Institute of Technology

学位级别：博士

Constrained low rank approximation is a general framework for data analysis, which usually has the advantage of being simple, fast, scalable and domain general. One of the most known constrained low rank approximation methods is nonnegative matrix factorization (NMF). This research studies the design and implementation of several variants of NMF for text, graph and hybrid data analytics. It will address challenges including solving new data analytics problems and improving the scalability of existing NMF algorithms. There are two major types of matrix representation of data: feature-data matrix and similarity matrix. Previous work showed successful application of standard NMF for feature-data matrix to areas such as text mining and image analysis, and Symmetric NMF (SymNMF) for similarity matrix to areas such as graph clustering and community detection. In this work, a divide-and-conquer strategy is applied to both methods to improve their time complexity from cubic growth with respect to the reduced low rank to linear growth, resulting in DC-NMF and HierSymNMF2 methods. Extensive experiments on large scale real world data show improved performance of these two methods. Furthermore, in this work NMF and SymNMF are combined into one formulation called JointNMF, to analyze hybrid data that contains both text content and connection structure information. Typical hybrid data where JointNMF can be applied includes paper/patent data where there are citation connections among content and email data where the sender/receipts relation is represented by a hypergraph and the email content is associated with hypergraph edges. An additional capability of the JointNMF is prediction of unknown network information which is illustrated using several real world problems such as citation recommendations of papers and activity/leader detection in organizations. This dissertation also includes brief discussions of relationship among different variants of NMF.

关键词： Constrained low rank approximation nonnegative matrix factorization Data analytics Content clustering Graph clustering

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GENERALIZED INTERVAL VALUED nonnegative matrix factorization 44

GENERALIZED INTERVAL VALUED NONNEGATIVE MATRIX FACTORIZATION

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44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Kohjima, Masahiro Matsubayashi, Tatsushi Toda, Hiroyuki NTT Corp NTT Serv Evolut Labs Tokyo Japan

ISBN: (纸本)9781479981311

In this paper, we propose a probabilistic model for analyzing the generalized interval valued matrix, a matrix that has scalar valued elements and bounded/unbounded interval valued elements. We derive a majorization minimization algorithm for parameter estimation and prove that the objective function is monotonically decreasing by the parameter update. An experiment shows that the proposed model well handles interval-valued elements and offers improved performance.

关键词： interval valued matrix nonnegative matrix factorization

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Active Learning with Bayesian nonnegative matrix factorization for Recommender Systems 27

Active Learning with Bayesian Nonnegative Matrix Factorizati...

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27th Signal Processing and Communications Applications Conference (SIU)

作者： Ayci, Gonul Koksal, Abdullatif Mutlu, M. Melih Suyunu, Burak Cemgil, A. Taylan Bogazici Univ Bilgisayar Muhendisligi Bolumu Istanbul Turkey

ISBN: (纸本)9781728119045

Active learning is a method of analyzing the observed data such that choosing the next observation will give the most information about the variable to be predicted. However, when observations are costly, one needs strategies to obtain informative data to arrive at accurate predictions with less data. In this study, we compare various observation sequence selection strategies on the matrix completion problem. We used Gibbs Sampling and Variational Bayes as inference mechanisms on the MovieLens dataset. Our results suggest that the Gibbs sampler coupled with the selection of the element with minimal observations on a row and column is the superior approach for the Bayesian nonnegative matrix factorization (NMF).

关键词： nonnegative matrix factorization Gibbs Sampler Variational Bayes Active Learning Recommendation Systems

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Laplace nonnegative matrix factorization with Application to Semi-supervised Audio Denoising 27

Laplace Nonnegative Matrix Factorization with Application to...

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27th European Signal Processing Conference (EUSIPCO)

作者： Tanji, Hiroki Murakami, Takahiro Kamata, Hiroyuki Meiji Univ Dept Elect & Bioinformat Sch Sci & Technol Tokyo Japan Univ Surrey Ctr Vis Speech & Signal Proc CVSSP Guildford Surrey England

ISBN: (纸本)9789082797039

This paper proposes two statistical models for the nonnegative matrix factorization (NMF) based on heavy-tailed distributions. In the NMF for acoustic signals, previous works justify the additivity of an observed spectrogram using the reproductive property of a probability density function. However, the effectiveness of these properties is not clear. Consequently, to construct a model robust to noise, statistical models based on heavy-tailed distributions are recently growing up. In this paper, as heavy-tailed models for the NMF, we introduce statistical models based on the complex Laplace distributions, and call them Laplace-NMF. Moreover, we derive convergence-guaranteed optimization algorithms to estimate parameters. From our formulation, a statistical interpretation of the Itakura-Saito (IS) divergence-based NMF is newly revealed. We confirm the effectiveness of Laplace-NMF in semi-supervised audio denoising.

关键词： complex Laplace distribution nonnegative matrix factorization majorization-minimization algorithm source separation

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