scholarly journals Computational Modeling of Hierarchically Polarized Groups by Structured Matrix Factorization

2021 ◽  
Vol 4 ◽  
Author(s):  
Dachun Sun ◽  
Chaoqi Yang ◽  
Jinyang Li ◽  
Ruijie Wang ◽  
Shuochao Yao ◽  
...  
Keyword(s):  
Computational Modeling ◽  
Matrix Factorization ◽  
State Of The Art ◽  
Language Model ◽  
Prior Art ◽  
Real World Datasets ◽  
Ablation Study ◽  
Core Issues ◽  
Polarization Detection ◽  
Non Negative Matrix Factorization

The paper extends earlier work on modeling hierarchically polarized groups on social media. An algorithm is described that 1) detects points of agreement and disagreement between groups, and 2) divides them hierarchically to represent nested patterns of agreement and disagreement given a structural guide. For example, two opposing parties might disagree on core issues. Moreover, within a party, despite agreement on fundamentals, disagreement might occur on further details. We call such scenarios hierarchically polarized groups. An (enhanced) unsupervised Non-negative Matrix Factorization (NMF) algorithm is described for computational modeling of hierarchically polarized groups. It is enhanced with a language model, and with a proof of orthogonality of factorized components. We evaluate it on both synthetic and real-world datasets, demonstrating ability to hierarchically decompose overlapping beliefs. In the case where polarization is flat, we compare it to prior art and show that it outperforms state of the art approaches for polarization detection and stance separation. An ablation study further illustrates the value of individual components, including new enhancements.

scholarly journals Using a Panchromatic Image to Improve Hyperspectral Unmixing

2020 ◽  
Vol 12 (17) ◽  
pp. 2834
Author(s):  
Simon Rebeyrol ◽  
Yannick Deville ◽  
Véronique Achard ◽  
Xavier Briottet ◽  
Stephane May
Keyword(s):  
Matrix Factorization ◽  
State Of The Art ◽  
Hyperspectral Data ◽  
Spectral Unmixing ◽  
Endmember Extraction ◽  
Local Approach ◽  
Hyperspectral Unmixing ◽  
Panchromatic Image ◽  
Art Methods ◽  
Non Negative Matrix Factorization

Hyperspectral unmixing is a widely studied field of research aiming at estimating the pure material signatures and their abundance fractions from hyperspectral images. Most spectral unmixing methods are based on prior knowledge and assumptions that induce limitations, such as the existence of at least one pure pixel for each material. This work presents a new approach aiming to overcome some of these limitations by introducing a co-registered panchromatic image in the unmixing process. Our method, called Heterogeneity-Based Endmember Extraction coupled with Local Constrained Non-negative Matrix Factorization (HBEE-LCNMF), has several steps: a first set of endmembers is estimated based on a heterogeneity criterion applied on the panchromatic image followed by a spectral clustering. Then, in order to complete this first endmember set, a local approach using a constrained non-negative matrix factorization strategy, is proposed. The performance of our method, in regards of several criteria, is compared to those of state-of-the-art methods obtained on synthetic and satellite data describing urban and periurban scenes, and considering the French HYPXIM/HYPEX2 mission characteristics. The synthetic images are built with real spectral reflectances and do not contain a pure pixel for each endmember. The satellite images are simulated from airborne acquisition with the spatial and spectral features of the mission. Our method demonstrates the benefit of a panchromatic image to reduce some well-known limitations in unmixing hyperspectral data. On synthetic data, our method reduces the spectral angle between the endmembers and the real material spectra by 46% compared to the Vertex Component Analysis (VCA) and N-finder (N-FINDR) methods. On real data, HBEE-LCNMF and other methods yield equivalent performance, but, the proposed method shows more robustness over the data sets compared to the tested state-of-the-art methods. Moreover, HBEE-LCNMF does not require one to know the number of endmembers.

scholarly journals Discrete Trust-aware Matrix Factorization for Fast Recommendation

2019 ◽  
Author(s):  
Guibing Guo ◽  
Enneng Yang ◽  
Li Shen ◽  
Xiaochun Yang ◽  
Xiaodong He
Keyword(s):  
Social Influence ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Social Relations ◽  
Real World ◽  
Matrix Factorization ◽  
State Of The Art ◽  
Proposed Model ◽  
Hamming Space ◽  
Real World Datasets

Trust-aware recommender systems have received much attention recently for their abilities to capture the influence among connected users. However, they suffer from the efficiency issue due to large amount of data and time-consuming real-valued operations. Although existing discrete collaborative filtering may alleviate this issue to some extent, it is unable to accommodate social influence. In this paper we propose a discrete trust-aware matrix factorization (DTMF) model to take dual advantages of both social relations and discrete technique for fast recommendation. Specifically, we map the latent representation of users and items into a joint hamming space by recovering the rating and trust interactions between users and items. We adopt a sophisticated discrete coordinate descent (DCD) approach to optimize our proposed model. In addition, experiments on two real-world datasets demonstrate the superiority of our approach against other state-of-the-art approaches in terms of ranking accuracy and efficiency.

Supervised Convolutional Matrix Factorization for Document Recommendation

2018 ◽  
Vol 17 (04) ◽  
pp. 1850018
Author(s):  
Huiting Liu ◽  
Chao Ling ◽  
Liangquan Yang ◽  
Peng Zhao
Keyword(s):  
Matrix Factorization ◽  
State Of The Art ◽  
Contextual Information ◽  
Auxiliary Information ◽  
Research Area ◽  
Information Item ◽  
Item Content ◽  
Real World Datasets ◽  
Content Information ◽  
Rating Information

Recently, document recommendation has become a very hot research area in online services. Since rating information is usually sparse with exploding growth of the numbers of users and items, conventional collaborative filtering-based methods degrade significantly in recommendation performance. To address this sparseness problem, auxiliary information such as item content information may be utilized. Convolution matrix factorization (ConvMF) is an appealing method, which tightly combines the rating and item content information. Although ConvMF captures contextual information of item content by utilizing convolutional neural network (CNN), the latent representation may not be effective when the rating information is very sparse. To address this problem, we generalize recent advances in supervised CNN and propose a novel recommendation model called supervised convolution matrix factorization (Super-ConvMF), which effectively combines the rating information, item content information and tag information into a unified recommendation framework. Experiments on three real-world datasets, two datasets come from MovieLens and the other one is from Amazon, show our model outperforms the state-of-the-art competitors in terms of the whole range of sparseness.

scholarly journals Probabilistic Non-Negative Matrix Factorization with Binary Components

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1189
Author(s):  
Xindi Ma ◽  
Jie Gao ◽  
Xiaoyu Liu ◽  
Taiping Zhang ◽  
Yuanyan Tang
Keyword(s):  
Matrix Factorization ◽  
Fixed Number ◽  
Weight Matrix ◽  
Low Rank ◽  
Factor Matrix ◽  
Binary Features ◽  
Rank Decomposition ◽  
Real World Datasets ◽  
Low Rank Decomposition ◽  
Non Negative Matrix Factorization

Non-negative matrix factorization is used to find a basic matrix and a weight matrix to approximate the non-negative matrix. It has proven to be a powerful low-rank decomposition technique for non-negative multivariate data. However, its performance largely depends on the assumption of a fixed number of features. This work proposes a new probabilistic non-negative matrix factorization which factorizes a non-negative matrix into a low-rank factor matrix with constraints and a non-negative weight matrix. In order to automatically learn the potential binary features and feature number, a deterministic Indian buffet process variational inference is introduced to obtain the binary factor matrix. Further, the weight matrix is set to satisfy the exponential prior. To obtain the real posterior distribution of the two factor matrices, a variational Bayesian exponential Gaussian inference model is established. The comparative experiments on the synthetic and real-world datasets show the efficacy of the proposed method.

scholarly journals SPMC: Socially-Aware Personalized Markov Chains for Sparse Sequential Recommendation

2017 ◽  
Author(s):  
Chenwei Cai ◽  
Ruining He ◽  
Julian McAuley
Keyword(s):  
Markov Chains ◽  
Social Relationships ◽  
Real World ◽  
Matrix Factorization ◽  
State Of The Art ◽  
Cold Start ◽  
Additional Information ◽  
New Methods ◽  
Real World Datasets ◽  
Sequential Information

Dealing with sparse, long-tailed datasets, and cold-start problems is always a challenge for recommender systems. These issues can partly be dealt with by making predictions not in isolation, but by leveraging information from related events; such information could include signals from social relationships or from the sequence of recent activities. Both types of additional information can be used to improve the performance of state-of-the-art matrix factorization-based techniques. In this paper, we propose new methods to combine both social and sequential information simultaneously, in order to further improve recommendation performance. We show these techniques to be particularly effective when dealing with sparsity and cold-start issues in several large, real-world datasets.

scholarly journals Predicting lincRNA-Disease Association in Heterogeneous Networks Using Co-regularized Non-negative Matrix Factorization

2021 ◽  
Vol 11 ◽  
Author(s):  
Yong Lin ◽  
Xiaoke Ma
Keyword(s):  
Matrix Factorization ◽  
Disease Gene ◽  
State Of The Art ◽  
Genetic Interaction ◽  
Interaction Network ◽  
Ribonucleic Acids ◽  
Genetic Interaction Network ◽  
Gene Associations ◽  
Shed Light ◽  
Non Negative Matrix Factorization

Long intergenic non-coding ribonucleic acids (lincRNAs) are critical regulators for many complex diseases, and identification of disease-lincRNA association is both costly and time-consuming. Therefore, it is necessary to design computational approaches to predict the disease-lincRNA associations that shed light on the mechanisms of diseases. In this study, we develop a co-regularized non-negative matrix factorization (aka Cr-NMF) to identify potential disease-lincRNA associations by integrating the gene expression of lincRNAs, genetic interaction network for mRNA genes, gene-lincRNA associations, and disease-gene associations. The Cr-NMF algorithm factorizes the disease-lincRNA associations, while the other associations/interactions are integrated using regularization. Furthermore, the regularization does not only preserve the topological structure of the lincRNA co-expression network, but also maintains the links “lincRNA → gene → disease.” Experimental results demonstrate that the proposed algorithm outperforms state-of-the-art methods in terms of accuracy on predicting the disease-lincRNA associations. The model and algorithm provide an effective way to explore disease-lncRNA associations.

scholarly journals Matrix Factorization for Evolution Data

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Xiao-Yu Huang ◽  
Xian-Hong Xiang ◽  
Wubin Li ◽  
Kang Chen ◽  
Wen-Xue Cai ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Graphical Model ◽  
State Of The Art ◽  
Empirical Studies ◽  
Factorization Problem ◽  
Time Points ◽  
Fitting Algorithm ◽  
Consecutive Time ◽  
Real World Datasets ◽  
Comparison Algorithms

We study a matrix factorization problem, that is, to find two factor matricesUandVsuch thatR≈UT×V, whereRis a matrix composed of the values of the objectsO1,O2,…,Onat consecutive time pointsT1,T2,…,Tt. We first present MAFED, a constrained optimization model for this problem, which straightforwardly performs factorization onR. Then based on the interplay of the data inU,V, andR, a probabilistic graphical model using the same optimization objects is constructed, in which structural dependencies of the data in these matrices are revealed. Finally, we present a fitting algorithm to solve the proposed MAFED model, which produces the desired factorization. Empirical studies on real-world datasets demonstrate that our approach outperforms the state-of-the-art comparison algorithms.

scholarly journals Fast SVM Trained by Divide-and-Conquer Anchors

2017 ◽  
Author(s):  
Meng Liu ◽  
Chang Xu ◽  
Chao Xu ◽  
Dacheng Tao
Keyword(s):  
Matrix Factorization ◽  
State Of The Art ◽  
Training Data ◽  
Divide And Conquer ◽  
Training Time ◽  
Learning Tasks ◽  
Multiple Datasets ◽  
Original Dataset ◽  
Representative Points ◽  
Non Negative Matrix Factorization

Supporting vector machine (SVM) is the most frequently used classifier for machine learning tasks. However, its training time could become cumbersome when the size of training data is very large. Thus, many kinds of representative subsets are chosen from the original dataset to reduce the training complexity. In this paper, we propose to choose the representative points which are noted as anchors obtained from non-negative matrix factorization (NMF) in a divide-and-conquer framework, and then use the anchors to train an approximate SVM. Our theoretical analysis shows that the solving the DCA-SVM can yield an approximate solution close to the primal SVM. Experimental results on multiple datasets demonstrate that our DCA-SVM is faster than the state-of-the-art algorithms without notably decreasing the accuracy of classification results.

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