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A Novel Hyperspectral Image Simulation Method Based on nonnegative matrix factorization

REMOTE SENSING 2019年第20期11卷

作者： Huang, Zehua Chen, Qi Chen, Qihao Liu, Xiuguo He, Hao China Univ Geosci Wuhan Sch Geog & Informat Engn Wuhan 430074 Hubei Peoples R China Xinjiang Univ Fac Civil Engn Urumqi 830047 Peoples R China

Hyperspectral (HS) images can provide abundant and fine spectral information on land surface. However, their applications may be limited by their narrow bandwidth and small coverage area. In this paper, we propose an HS image simulation method based on nonnegative matrix factorization (NMF), which aims at generating HS images using existing multispectral (MS) data. Our main novelty is proposing a spectral transformation matrix and new simulation method. First, we develop a spectral transformation matrix that transforms HS endmembers into MS endmembers. Second, we utilize an iteration scheme to optimize the HS and MS endmembers. The test MS image is then factorized by the MS endmembers to obtain the abundance matrix. The result image is constructed by multiplying the abundance matrix by the HS endmembers. Experiments prove that our method provides high spectral quality by combining prior spectral endmembers. The iteration schemes reduce the simulation error and improve the accuracy of the results. In comparative trials, the spectral angle, RMSE, and correlation coefficient of our method are 5.986, 284.6, and 0.905, respectively. Thus, our method outperforms other simulation methods.

关键词： hyperspectral image hyperspectral image simulation pseudo-hyperspectral imagery nonnegative matrix factorization spectral reconstruction

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nonnegative matrix factorization Requires Irrationality

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SIAM JOURNAL ON APPLIED ALGEBRA AND GEOMETRY 2017年第1期1卷 285-307页

作者： Chistikov, Dmitry Kiefer, Stefan Marusic, Ines Shirmohammadi, Mahsa Worrell, James Max Planck Inst Software Syst MPI SWS D-67663 Kaiserslautern Germany Max Planck Inst Software Syst MPI SWS D-66123 Saarbrucken Germany Univ Warwick Coventry CV4 7AL W Midlands England Univ Oxford Oxford OX1 3QD England

nonnegative matrix factorization (NMF) is the problem of decomposing a given nonnegative is n x m so matrix M into a product of a nonnegative is n x d matrix W and a nonnegative d x m to matrix H. A longstanding open question, posed by Cohen and Rothblum in 1993, is whether a rational matrix M always has an NMF of minimal inner dimension d whose factors W and H are also rational. We answer this question negatively, by exhibiting a matrix for which W and H require irrational entries.

关键词： nonnegative matrix factorization nonnegative rank

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Predicting and understanding comprehensive drug-drug interactions via semi-nonnegative matrix factorization

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BMC SYSTEMS BIOLOGY 2018年第SUPPL 1期12卷 101-110页

作者： Yu, Hui Mao, Kui-Tao Shi, Jian-Yu Huang, Hua Chen, Zhi Dong, Kai Yiu, Siu-Ming Northwestern Polytech Univ Sch Comp Sci Xian Shaanxi Peoples R China Northwestern Polytech Univ Sch Life Sci Xian Shaanxi Peoples R China Northwestern Polytech Univ Sch Software & Microelect Xian Shaanxi Peoples R China Peoples Hosp Jiangxi Prov Dept Crit Care Med Nanchang Jiangxi Peoples R China Univ Hong Kong Dept Comp Sci Hong Kong Hong Kong Peoples R China

Background: Drug-drug interactions (DDIs) always cause unexpected and even adverse drug reactions. It is important to identify DDIs before drugs are used in the market. However, preclinical identification of DDIs requires much money and time. Computational approaches have exhibited their abilities to predict potential DDIs on a large scale by utilizing pre-market drug properties (e.g. chemical structure). Nevertheless, none of them can predict two comprehensive types of DDIs, including enhancive and degressive DDIs, which increases and decreases the behaviors of the interacting drugs respectively. There is a lack of systematic analysis on the structural relationship among known DDIs. Revealing such a relationship is very important, because it is able to help understand how DDIs occur. Both the prediction of comprehensive DDIs and the discovery of structural relationship among them play an important guidance when making a co-prescription. Results: In this work, treating a set of comprehensive DDIs as a signed network, we design a novel model (DDINMF) for the prediction of enhancive and degressive DDIs based on semi-nonnegative matrix factorization. Inspiringly, DDINMF achieves the conventional DDI prediction (AUROC = 0.872 and AUPR = 0.605) and the comprehensive DDI prediction (AUROC = 0.796 and AUPR = 0.579). Compared with two state-of-the-art approaches, DDINMF shows it superiority. Finally, representing DDIs as a binary network and a signed network respectively, an analysis based on NMF reveals crucial knowledge hidden among DDIs. Conclusions: Our approach is able to predict not only conventional binary DDIs but also comprehensive DDIs. More importantly, it reveals several key points about the DDI network: (1) both binary and signed networks show fairly clear clusters, in which both drug degree and the difference between positive degree and negative degree show significant distribution;(2) the drugs having large degrees tend to have a larger difference between pos

关键词： Drug-drug interaction nonnegative matrix factorization Regression Network community Balance theory

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nonnegative matrix factorization algorithms for link prediction in temporal networks using graph communicability

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PATTERN RECOGNITION 2017年 71卷 361-374页

作者： Ma, Xiaoke Sun, Penggang Qin, Guimin Xidian Univ Sch Comp Sci & Technol 2 South Taibai Rd Xian Shaanxi Peoples R China Xidian Univ Sch Software 2 South Taibai Rd Xian Shaanxi Peoples R China

Networks derived from many disciplines, such as social relations, web contents, and cancer progression, are temporal and incomplete. Link prediction in temporal networks is of theoretical interest and practical significance because spurious links are critical for investigating evolving mechanisms. In this study, we address the temporal link prediction problem in networks, i.e. predicting links at time T + 1 based on a given temporal network from time 1 to T. To address the relationships among matrix decomposition based algorithms, we prove the equivalence between the eigendecomposition and nonnegative matrix factorization (NMF) algorithms, which serves as the theoretical foundation for designing NMF-based algorithms for temporal link prediction. A novel NMF-based algorithm is proposed based on such equivalence. The algorithm factorizes each network to obtain features using graph communicability, and then collapses the feature matrices to predict temporal links. Compared with state-of-the-art methods, the proposed algorithm exhibits significantly improved accuracy by avoiding the collapse of temporal networks. Experimental results of a number of artificial and real temporal networks illustrate that the proposed method is not only more accurate but also more robust than state-of-the-art approaches. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Dynamic networks nonnegative matrix factorization Eigenvalues and eigenvector Temporal link prediction

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Adaptive Clustering via Symmetric nonnegative matrix factorization of the Similarity matrix

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ALGORITHMS 2019年第10期12卷

作者： Favati, Paola Lotti, Grazia Menchi, Ornella Romani, Francesco CNR IIT Via G Moruzzi 1 I-56124 Pisa Italy Univ Parma Dipartimento Matemat Parco Area Sci 53-A I-43124 Parma Italy Univ Pisa Dipartimento Informat Largo Pontecorvo 3 I-56127 Pisa Italy

The problem of clustering, that is, the partitioning of data into groups of similar objects, is a key step for many data-mining problems. The algorithm we propose for clustering is based on the symmetric nonnegative matrix factorization (SymNMF) of a similarity matrix. The algorithm is first presented for the case of a prescribed number k of clusters, then it is extended to the case of a not a priori given k. A heuristic approach improving the standard multistart strategy is proposed and validated by the experimentation.

关键词： clustering nonnegative matrix factorization adaptive strategy

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A Unified Convergence Analysis of the Multiplicative Update Algorithm for Regularized nonnegative matrix factorization

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2018年第1期66卷 129-138页

作者： Zhao, Renbo Tan, Vincent Y. F. NUS Dept Elect & Comp Engn Singapore 117583 Singapore NUS Dept Ind Syst Engn & Management Singapore 117576 Singapore NUS Dept Math Singapore 119076 Singapore

The multiplicative update (MU) algorithm has been extensively used to estimate the basis and coefficient matrices in nonnegative matrix factorization (NMF) problems under a wide range of divergences and regularizers. However, theoretical convergence guarantees have only been derived for a few special divergences without regularization. In this work, we provide a conceptually simple, self-contained, and unified proof for the convergence of the MU algorithm applied on NMF with a wide range of divergences and regularizers. Our main result shows the sequence of iterates (i.e., pairs of basis and coefficient matrices) produced by the MU algorithm converges to the set of stationary points of the nonconvex NMF optimization problem. Our proof strategy has the potential to open up new avenues for analyzing similar problems in machine learning and signal processing.

关键词： nonnegative matrix factorization multiplicative update algorithm convergence analysis nonconvex optimization stationary points

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An efficient nonnegative matrix factorization model for finding cancer associated genes by integrating data from genome, transcriptome and interactome 52

An efficient nonnegative matrix factorization model for find...

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52nd Annual Conference on Information Sciences and Systems (CISS)

作者： Xi, Jianing Li, Ao Wang, Minghui Univ Sci & Technol China Sch Informat Sci & Technol Hefei Anhui Peoples R China

ISBN: (纸本)9781538605790

The recent development of sequencing technology has offered us the opportunity of regarding cancer genome as a biological system and finding cancer associated genes computationally. Since some cancer associated genes may be omitted when using only mutation frequencies of genes, many recent studies use both mutation data from genome and gene interaction network from interactome to detect cancer associated genes. However, transcriptome information is not exploited in the task of finding cancer associated genes, which is also highly related to cancer. In this article, we introduce an nonnegative matrix factorization based model to find cancer associated genes, which can efficiently integrate information from genome, transcriptome and interactome via graph Laplacian regularization. When we compare our method with two existing methods and apply these methods on three independent cancer datasets, our method outperforms the existing methods for the evaluation of two well-curated known cancer genes.

关键词： biological system data integration nonnegative matrix factorization Laplacian regularization

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Speech Enhancement Based on Codebook Constrained nonnegative matrix factorization 6

Speech Enhancement Based on Codebook Constrained Nonnegative...

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International Conference on Audio, Language and Image Processing (ICALIP)

作者： Bai, Zhigang Bao, Changchun Yan, Bofang Beijing Univ Technol Fac Informat Technol Speech & Audio Signal Proc Lab Beijing 100124 Peoples R China

ISBN: (纸本)9781538651957

In this paper, we present a novel approach for speech enhancement based on nonnegative matrix factorization (NMF) with the speech magnitude spectrum constrained by a codebook. First, we utilize a codebook to model the magnitude spectrum of clean speech and a speech magnitude spectrum codebook is trained containing the priori information of speech. Second, a classic noise estimation algorithm is employed to estimate the power spectral density (PSD) of noise to avoid noise classification. Then, we obtain the basis matrix of the noisy speech by combining the noise spectral with the optimal entry from the speech codebook. The magnitude spectrum of the noisy speech is decomposed by performing NMF and the estimated speech and noise components are obtained. Finally, the obtained speech and noise components are used to enhance the noisy speech. Moreover, the residual noise is further eliminated by applying the speech presence probability (SPP). The objective evaluations demonstrate that the proposed algorithm outperforms the conventional NMF based method for all the evaluated noise types at various input signal-to-noise ratios.

关键词： nonnegative matrix factorization NMF priori codebook speech enhancement

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Supervised nonnegative matrix factorization to Predict ICU Mortality Risk

Supervised Nonnegative Matrix Factorization to Predict ICU M...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM) - Human Genomics

作者： Chao, Guoqing Mao, Chengsheng Wang, Fei Zhao, Yuan Luo, Yuan Northwestern Univ Feinberg Sch Med Chicago IL 60611 USA Cornell Univ Weill Cornell Med New York NY 10021 USA

ISBN: (纸本)9781538654880

ICU mortality risk prediction is a tough yet important task. On one hand, due to the complex temporal data collected, it is difficult to identify the effective features and interpret them easily;on the other hand, good prediction can help clinicians take timely actions to prevent the mortality. These correspond to the interpretability and accuracy problems. Most existing methods lack of the interpretability, but recently Sub-graph Augmented nonnegative matrix factorization (SANMF) has been successfully applied to time series data to provide a path to interpret the features well. Therefore, we adopted this approach as the backbone to analyze the patient data. One limitation of the original SANMF method is its poor prediction ability due to its unsupervised nature. To deal with this problem, we proposed a supervised SANMF algorithm by integrating the logistic regression loss function into the NMF framework and solved it with an alternating optimization procedure. We used the simulation data to verify the effectiveness of this method, and then we applied it to ICU mortality risk prediction and demonstrated its superiority over other conventional supervised NMF methods.

关键词： nonnegative matrix factorization Logistic regression Supervised learning Representation ICU mortality risk

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CONSTRAINED nonnegative matrix factorization FOR ROBUST HYPERSPECTRAL UNMIXING 38

CONSTRAINED NONNEGATIVE MATRIX FACTORIZATION FOR ROBUST HYPE...

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38th IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Feng, Fan Deng, Chenwei Wang, Wenzheng Dai, Jiahui Li, Zhenzhen Zhao, Baojun Beijing Inst Technol Beijing Key Lab Embedded Real Time Informat Proc Beijing Peoples R China

ISBN: (纸本)9781538671504

Hyperspectral unmixng (HU) is an essential step for hyperspectral image (HSI) analysis. In real HSI, there often are abnormal fluctuations existing in specific bands, which can be described as sparse noise. This type of corruption will seriously disrupt the hyperspectral image quality, causing extra difficulties during unmixing process. However, the influence of sparse noise is often ignored by existing unmixing methods, which leads to the reduction of robustness and accuracy for HU tasks. Therefore, we propose a new unmixing model which takes noise corruption into consideration. By designing and imposing constraints considering the sparsity of noise, properties of endmember and abundance on nonnegative matrix factorization (NMF), the proposed method can resist the sparse noise and achieve more robust and accurate unmixing results. Adequate experiments have been conducted on both synthetic and real hyperspectral data. And the results confirm the superiority of proposed method compared to state-of-the-art methods.

关键词： Hyperspectral unmixing robust nonnegative matrix factorization sparse noise constraint

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