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检索条件"主题词=Nonnegative Matrix factorization"

共 1480 条记录，以下是311-320 订阅

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Expectation-Maximization Algorithms for Itakura-Saito nonnegative matrix factorization 19

Expectation-Maximization Algorithms for Itakura-Saito Nonneg...

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19th Annual Conference of the International-Speech-Communication-Association (INTERSPEECH 2018)

作者： Magron, Paul Virtanen, Tuomas Tampere Univ Technol Signal Proc Lab Tampere Finland

ISBN: (纸本)9781510872219

This paper presents novel expectation-maximization (EM) algorithms for estimating the nonnegative matrix factorization model with Itakura-Saito divergence. Indeed, the common EM based approach exploits the space-alternating generalized EM (SAGE) variant of EM but it usually performs worse than the conventional multiplicative algorithm. We propose to explore more exhaustively those algorithms, in particular the choice of the methodology (standard EM or SAGE variant) and the latent variable set (full or reduced). We then derive four EM-based algorithms, among which three are novel. Speech separation experiments show that one of those novel algorithms using a standard EM methodology and a reduced set of latent variables outperforms its SAGE variants and competes with the conventional multiplicative algorithm.

关键词： Expectation-maximization nonnegative matrix factorization Itakura-Saito divergence audio source separation

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A nonnegative matrix factorization Approach for Multiple Local Community Detection 10

A Nonnegative Matrix Factorization Approach for Multiple Loc...

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IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

作者： Kamuhanda, Dany He, Kun Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan Hubei Peoples R China

ISBN: (纸本)9781538660515

Existing works on local community detection in social networks focus on finding one single community a few seed members are most likely to be in. In this work, we address a much harder problem of multiple local community detection and propose a nonnegative matrix factorization algorithm for finding multiple local communities for a single seed chosen randomly in multiple ground truth communities. The number of detected communities for the seed is determined automatically by the algorithm. We first apply a Breadth-First Search to sample the input graph up to several levels depending on the network density. We then use nonnegative matrix factorization on the adjacency matrix of the sampled subgraph to estimate the number of communities, and then cluster the nodes of the subgraph into communities. Our proposed method differs from the existing NMF-based community detection methods as it does not use "argmax" function to assign nodes to communities. Our method has been evaluated on real-world networks and shows good accuracy as evaluated by the F-1 score when comparing with the state-of-the-art local community detection algorithm.

关键词： Community Detection nonnegative matrix factorization Multiple Local Communities

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Elastic nonnegative matrix factorization 18

Elastic Nonnegative Matrix Factorization

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18th IEEE International Conference on Data Mining Workshops (ICDMW)

作者： Ballen, Peter Guha, Sudipto Univ Penn Philadelphia PA 19104 USA

ISBN: (纸本)9781538692882

nonnegative matrix factorization factors a large matrix into smaller nonnegative components. Non-negative models are often more amenable to interpretation vis-a-vis standard principal component analysis where negative entries may not correspond to any physical process. Non-negative matrix factorization has been used widely in a variety of data mining settings, in a multitude of disciplines, including the famous Netflix challenge. Finding optimum solution to these problems are intractable. Iterative updates are widely used to find a local optima in most algorithms, but many of these updates are unstable and lack strong convergence guarantees, including several well known ones. We provide a technique that guarantees convergence even under distributed updates (with mild restrictions). Moreover there has been extensive research in many variants of nonnegative matrix factorization in the presence of noise. None of these robust algorithms have guaranteed convergence to even a local optima. We provide an algorithm that adheres to the general analysis technique and addresses noise and contamination. The algorithm is implemented in an elastic distributed system and provides a better quality of factorization than many alternative approaches on a variety of datasets

关键词： nonnegative matrix factorization algorithms robust algorithms

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Adaptive Multi-view Clustering Based on nonnegative matrix factorization and Pairwise Co-regularization 9

Adaptive Multi-view Clustering Based on Nonnegative Matrix F...

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9th International Conference on Graphic and Image Processing (ICGIP)

作者： Zhang, Tianzhen Wang, Xiumei Gao, Xinbo Xidian Univ Sch Elect Engn Video & Image Proc Syst VIPS Lab Xian Shaanxi Peoples R China

ISBN: (数字)9781510617421

ISBN: (纸本)9781510617421

Nowadays, several datasets are demonstrated by multi-view, which usually include shared and complementary information. Multi-view clustering methods integrate the information of multi-view to obtain better clustering results. nonnegative matrix factorization has become an essential and popular tool in clustering methods because of its interpretation. However, existing nonnegative matrix factorization based multi-view clustering algorithms do not consider the disagreement between views and neglects the fact that different views will have different contributions to the data distribution. In this paper, we propose a new multi-view clustering method, named adaptive multi-view clustering based on nonnegative matrix factorization and pairwise co-regularization. The proposed algorithm can obtain the parts-based representation of multi-view data by nonnegative matrix factorization. Then, pairwise co-regularization is used to measure the disagreement between views. There is only one parameter to auto learning the weight values according to the contribution of each view to data distribution. Experimental results show that the proposed algorithm outperforms several state-of-the-arts algorithms for multi-view clustering.

关键词： Multi-view clustering nonnegative matrix factorization adaptive pairwise co-regularization

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MULTI-TASK nonnegative matrix factorization 25

MULTI-TASK NONNEGATIVE MATRIX FACTORIZATION

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25th IEEE International Conference on Image Processing (ICIP)

作者： An, Shounan Netmarble nARC Netmarble Revolut Ctr Seoul South Korea

ISBN: (纸本)9781479970612

Multi-task learning is a prominent methodology for learning relationship of multiple related tasks to improve the overall generalization performance. In literature, there are several works of applying nonnegative matrix factorization (NMF) to multi-task clustering (MTC) for its useful behavior as a clustering method. When NMF is applied to a data matrix, the matrix is decomposed into a product of two factor matrices, where one corresponds to basis matrix (cluster centers) and the other is associated with encoding matrix (cluster indicator variables). In this paper, we consider clustering activities of daily living (ADL) as a single task. Given ADL data recorded from different people under the same conditions, our motivation is that related tasks share many common action patterns, while each task also has its own unique action characteristics. Our goal is to learn cross-task basis matrices that capture shared information across multiple related tasks, while try to find within-task basis matrices encoding task-dependent characteristics, which will not appear in any other tasks. To this end, we propose a method for multi-task nonnegative matrix factorization (MTNMF), which enforces cross-task bases to be as close as possible, on the contrary, keeps within-task bases as far as possible. We develop efficient multiplicative update rules to learn MTNMF and numerical experiments on ADL dataset indicate that MTNMF improves clustering performance in multi-task environment.

关键词： multi-task clustering nonnegative matrix factorization daily activity recognition

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A nonnegative matrix factorization Based Approach to Extract Aspects from Product Reviews 1st

A Nonnegative Matrix Factorization Based Approach to Extract...

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1st International Conference on Advanced Computational and Communication Paradigms (ICACCP)

作者： Barman, Debaditya Chowdhury, Nirmalya Visva Bharati Dept Comp & Syst Sci Bolpur India Jadavpur Univ Dept Comp Sci & Engn Kolkata India

ISBN: (纸本)9789811082375;9789811082368

Due to the unstructured nature of review text, it is very hard to develop an automated opinion mining application to compare various product models based on their various aspects to make a purchase decision. Over the year, various data mining techniques have been proposed to extract aspects of the products. In this paper, we have proposed a technique based on the nonnegative matrix factorization to extract aspects of a product category. Performance of our proposed method has been compared with a very popular aspect extraction technique based on probabilistic latent semantic analysis. We have also given a comparison between common aspects of a particular model under a specific product category from various manufacturers. These comparisons are based on the sentiments expressed by the users on these aspects. These sentiments expressed in various aspects have been extracted using an unsupervised technique.

关键词： Aspect extraction Opinion mining nonnegative matrix factorization E-commerce

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Active Selection of Elements for Bayesian nonnegative matrix factorization 26

Active Selection of Elements for Bayesian Nonnegative Matrix...

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26th IEEE Signal Processing and Communications Applications Conference (SIU)

作者： Suyunu, Burak Ayci, Gonul Cemgil, A. Taylan Bogazici Univ Bilgisayar Muhendisligi Bolumu Istanbul Turkey

ISBN: (纸本)9781538615010

In classical matrix completion problems, you can divide the matrix into two groups as observed and unknown elements. In this study, the matrices are composed of three different groups: observed data that is known and accessible at any time without any expense, unknown data that we are trying to predict, and data that is currently unknown but can be queried when desired. When the data in the last group is queried for the first time, a cost arises. From this observation, we want to estimate the unknown data in the second group with the least error by making as few queries as possible. Our goal is to choose the observations we are trying to question wisely. In this study, observation sequence selections were defined and compared in the MovieLens data set.

关键词： nonnegative matrix factorization Gibbs Sampler Active Learning Recommendation Systems Bayesian Inference via Monte Carlo

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Toward Finding Optimal Source Dictionaries for Single Channel Music Source Separation Using nonnegative matrix factorization 8

Toward Finding Optimal Source Dictionaries for Single Channe...

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8th IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Rathnayake, Bhathiya Weerakoon, K. M. K. Godaliyadda, G. M. R., I Ekanayake, M. P. B. Sri Lanka Technol Campus ORIS Padukka 10500 CO Sri Lanka Univ Peradeniya Dept Elect & Elect Engn Peradeniya 20400 KY Sri Lanka

ISBN: (纸本)9781538692769

Source signal separation from single channel polyphonic music has been attracting conspicuous attention from the audio signal processing community recently. In this paper, we propose a single channel music source separation strategy in an informed setting using nonnegative matrix factorization (NMF). For the first time in the audio source separation context, we adopt the local NMF (LNMF) to distill source dictionaries and experimentally demonstrate the outstanding performance of LNMF over conventional NMF in music source separation. We further extend our work alleviating two fundamental drawbacks of NMF: the randomness of the NMF rank selection which leads to infinitely many possible representations of data and the arbitrary initializations of NMF algorithms which give rise to different source dictionaries for different initial conditions even under a fixed NMF rank. For each music source, using a stability criterion, we identify the stablest NMF rank for which the resulting dictionary is the most reproducible over multiple NMF runs with different initializations. Then we propose to obtain a refined dictionary using clustering approaches over dictionaries obtained for different NNIF initializations under the stablest NMF rank. Source separation results which ensure the feasibility of the proposed methods are provided.

关键词： Dictionary learning music information retrieval nonnegative matrix factorization NMF rank single channel source separation

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Prediction of Drug-Disease Associations and Their Effects by Signed Network-Based nonnegative matrix factorization

Prediction of Drug-Disease Associations and Their Effects by...

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

作者： Zhang, Wen Huang, Feng Yue, Xiang Lu, Xiaoting Yang, Weitai Li, Zhishuai Liu, Feng Huazhong Agr Univ Coll Informat Wuhan 430070 Hubei Peoples R China Wuhan Univ Sch Comp Sci Wuhan 430072 Hubei Peoples R China Ohio State Univ Dept Comp Sci & Engn Columbus OH 43210 USA

ISBN: (纸本)9781538654880

Predicting drug-disease associations using computational methods benefits drug repositioning. Drug-disease associations are events that drugs exert effects on diseases, there are different effects about drug-disease associations. For example, drug disease associations are annotated as therapeutic or marker/mechanism (non-therapeutic) in Comparative Toxicogenomics database (CTD). However, existing association prediction methods ignore effects that drugs exert on diseases. In this paper, we propose a signed network-based nonnegative matrix factorization method (SNNMF) to predict drug-disease associations and their effects. First, drug-disease associations are represented as a signed bipartite network with two types of links for therapeutic effects and non-therapeutic effects. After decomposing the network into two subnetworks, SNNMF aims to approximate the association matrix of each subnetwork by two nonnegative matrices, which are low-dimensional latent representations for drugs and diseases respectively, and diseases in two subnetworks share the same latent representations. In the computational experiments, SNNMF performs well in predicting effects of drug-disease associations. Moreover, SNNMF accurately predicts drug-disease associations and outperforms existing association prediction methods. Case studies show that SNNMF helps to find out novel drug-disease associations that are not included in CTD, and simultaneously predicts their therapeutic effects.

关键词： drug-disease association signed network nonnegative matrix factorization

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HNGRNMF : Heterogeneous Network-based Graph Regularized nonnegative matrix factorization for predicting events of microbe-disease associations

HNGRNMF : Heterogeneous Network-based Graph Regularized Nonn...

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

作者： Zhang, Wen Lu, Xiaoting Yang, Weitai Huang, Feng Wang, Binlu Wang, Alan Zhao, Qi Huazhong Agr Univ Coll Informat Wuhan 430070 Hubei Peoples R China Wuhan Univ Sch Comp Sci Wuhan 430072 Hubei Peoples R China Wuhan Univ Sch Phys & Technol Wuhan 430072 Hubei Peoples R China Liaoning Univ Sch Math Shenyang Liaoning Peoples R China Res Ctr Comp Simulating & Informat Proc Biomacrom Shenyang Liaoning Peoples R China

ISBN: (纸本)9781538654880

Identifting the microbe-disease associations provides a great insight into the mechanism that microbes cause diseases at the molecular level. Existing computational methods utilized known microbe-disease associations, microbe features and disease features to predict novel associations..Howevet there exist different events related to microbe-disease associations, for example, the species abundance of a microbe in bodies will increase or decrease when patients have a disease. In this papet;we propose a heterogeneous network-based graph regularized nonnegative matrix factorization (HATGRNIIF) to predict microbe-disease associations as well as the events. HNGRIVIVIF constructs a heterogeneous network based on observed microbe disease association-related events: increase/decrease on species abundance of microbes, and extracts two event matrices;from the heterogeneous network, and then decomposes matrices into the common low-rank disease space and two different low-rank microbe spaces under the frame ofnonnegative matrix factorization. In addition, microbe-microbe similarities and disease-disease similarities are integrated, respectively and incorporated into HATGRNME The experiment results demonstrate that HNGRNMF accurately predicts microbe-disease associations and their events when evaluated by leave-one-out cross validation and 5-fold cross validation.

关键词： microbe-disease associations heterogeneous network nonnegative matrix factorization

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