scholarly journals Parallel Nonnegative Matrix Factorization with Manifold Regularization

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Fudong Liu ◽  
Zheng Shan ◽  
Yihang Chen
Keyword(s):  
Distributed Computing ◽  
Matrix Factorization ◽  
Large Scale ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Computing System ◽  
Construction Method ◽  
Manifold Regularization ◽  
Single Node ◽  
Text Corpora

Nonnegative matrix factorization (NMF) decomposes a high-dimensional nonnegative matrix into the product of two reduced dimensional nonnegative matrices. However, conventional NMF neither qualifies large-scale datasets as it maintains all data in memory nor preserves the geometrical structure of data which is needed in some practical tasks. In this paper, we propose a parallel NMF with manifold regularization method (PNMF-M) to overcome the aforementioned deficiencies by parallelizing the manifold regularized NMF on distributed computing system. In particular, PNMF-M distributes both data samples and factor matrices to multiple computing nodes instead of loading the whole dataset in a single node and updates both factor matrices locally on each node. In this way, PNMF-M succeeds to resolve the pressure of memory consumption for large-scale datasets and to speed up the computation by parallelization. For constructing the adjacency matrix in manifold regularization, we propose a two-step distributed graph construction method, which is proved to be equivalent to the batch construction method. Experimental results on popular text corpora and image datasets demonstrate that PNMF-M significantly improves both scalability and time efficiency of conventional NMF thanks to the parallelization on distributed computing system; meanwhile it significantly enhances the representation ability of conventional NMF thanks to the incorporated manifold regularization.

scholarly journals Local Topic Discovery via Boosted Ensemble of Nonnegative Matrix Factorization

2017 ◽  
Author(s):  
Sangho Suh ◽  
Jaegul Choo ◽  
Joonseok Lee ◽  
Chandan K. Reddy
Keyword(s):  
Matrix Factorization ◽  
Topic Modeling ◽  
Large Scale ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Gradient Boosting ◽  
Ensemble Model ◽  
High Quality ◽  
Residual Matrix ◽  
The Given

Nonnegative matrix factorization (NMF) has been increasingly popular for topic modeling of large-scale documents. However, the resulting topics often represent only general, thus redundant information about the data rather than minor, but potentially meaningful information to users. To tackle this problem, we propose a novel ensemble model of nonnegative matrix factorization for discovering high-quality local topics. Our method leverages the idea of an ensemble model to successively perform NMF given a residual matrix obtained from previous stages and generates a sequence of topic sets. The novelty of our method lies in the fact that it utilizes the residual matrix inspired by a state-of-the-art gradient boosting model and applies a sophisticated local weighting scheme on the given matrix to enhance the locality of topics, which in turn delivers high-quality, focused topics of interest to users.

Constructing large-scale cortical brain networks from scalp EEG with Bayesian nonnegative matrix factorization

2020 ◽  
Vol 125 ◽  
pp. 338-348 ◽  
Author(s):  
Chanlin Yi ◽  
Chunli Chen ◽  
Yajing Si ◽  
Fali Li ◽  
Tao Zhang ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Large Scale ◽  
Nonnegative Matrix Factorization ◽  
Brain Networks ◽  
Nonnegative Matrix ◽  
Scalp Eeg

A Novel Image Retrieval System Based on Histogram Factorization and Contextual Similarity Learning

2013 ◽  
Vol 380-384 ◽  
pp. 4148-4151 ◽  
Author(s):  
Sivakolundu Jayasekara ◽  
Hithanadura Dassanayake ◽  
Anil Fernando
Keyword(s):  
Image Retrieval ◽  
Matrix Factorization ◽  
Large Scale ◽  
Nonnegative Matrix Factorization ◽  
Retrieval System ◽  
Nonnegative Matrix ◽  
Bag Of Words ◽  
Similarity Learning ◽  
Query Image ◽  
Image Retrieval System

Image retrieval has been a top topic in the field of both computer vision and machine learning for a long time. Content based image retrieval, which tries to retrieve images from a database visually similar to a query image, has attracted much attention. Two most important issues of image retrieval are the representation and ranking of the images. Recently, bag-of-words based method has shown its power as a representation method. Moreover, nonnegative matrix factorization is also a popular way to represent the data samples. In addition, contextual similarity learning has also been studied and proven to be an effective method for the ranking problem. However, these technologies have never been used together. In this paper, we developed an effective image retrieval system by representing each image using the bag-of-words method as histograms, and then apply the nonnegative matrix factorization to factorize the histograms, and finally learn the ranking score using the contextual similarity learning method. The proposed novel system is evaluated on a large scale image database and the effectiveness is shown.

scholarly journals An Overlapping Community Detection Approach in Ego-Splitting Networks Using Symmetric Nonnegative Matrix Factorization

Symmetry ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 869
Author(s):  
Mingqing Huang ◽  
Qingshan Jiang ◽  
Qiang Qu ◽  
Abdur Rasool
Keyword(s):  
Community Detection ◽  
Matrix Factorization ◽  
Large Scale ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Overlapping Community Detection ◽  
Detection Approach ◽  
Overlapping Community ◽  
Large Scale Networks ◽  
Symmetric Nonnegative Matrix Factorization

Overlapping clustering is a fundamental and widely studied subject that identifies all densely connected groups of vertices and separates them from other vertices in complex networks. However, most conventional algorithms extract modules directly from the whole large-scale graph using various heuristics, resulting in either high time consumption or low accuracy. To address this issue, we develop an overlapping community detection approach in Ego-Splitting networks using symmetric Nonnegative Matrix Factorization (ESNMF). It primarily divides the whole network into many sub-graphs under the premise of preserving the clustering property, then extracts the well-connected sub-sub-graph round each community seed as prior information to supplement symmetric adjacent matrix, and finally identifies precise communities via nonnegative matrix factorization in each sub-network. Experiments on both synthetic and real-world networks of publicly available datasets demonstrate that the proposed approach outperforms the state-of-the-art methods for community detection in large-scale networks.

scholarly journals Localized semi-nonnegative matrix factorization (LocaNMF) of widefield calcium imaging data

2019 ◽  
Author(s):  
Shreya Saxena ◽  
Ian Kinsella ◽  
Simon Musall ◽  
Sharon H. Kim ◽  
Jozsef Meszaros ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Calcium Imaging ◽  
Large Scale ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Brain Regions ◽  
Video Data ◽  
Imaging Data ◽  
Dorsal Cortex ◽  
Experimental Conditions

Widefield calcium imaging enables recording of large-scale neural activity across the mouse dorsal cortex. In order to examine the relationship of these neural signals to the resulting behavior, it is critical to demix the recordings into meaningful spatial and temporal components that can be mapped onto well-defined brain regions. However, no current tools satisfactorily extract the activity of the different brain regions in individual mice in a data-driven manner, while taking into account mouse-specific and preparation-specific differences. Here, we introduce Localized semi-Nonnegative Matrix Factorization (LocaNMF), a method that efficiently decomposes widefield video data and allows us to directly compare activity across multiple mice by outputting mouse-specific localized functional regions that are significantly more interpretable than more traditional decomposition techniques. Moreover, it provides a natural subspace to directly compare correlation maps and neural dynamics across different behaviors, mice, and experimental conditions, and enables identification of task- and movement-related brain regions.

scholarly journals Large-scale topic community mining based on distributed nonnegative matrix factorization

2016 ◽  
Vol 46 (6) ◽  
pp. 714-728
Author(s):  
Hai LIU ◽  
Atiao YANG ◽  
Gansen ZHAO ◽  
Chaobo HE ◽  
Yong TANG ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Large Scale ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Community Mining

Convex nonnegative matrix factorization with manifold regularization

2015 ◽  
Vol 63 ◽  
pp. 94-103 ◽  
Author(s):  
Wenjun Hu ◽  
Kup-Sze Choi ◽  
Peiliang Wang ◽  
Yunliang Jiang ◽  
Shitong Wang
Keyword(s):  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Manifold Regularization

Cross-scene hyperspectral image classification based on DWT and manifold-constrained subspace learning

2017 ◽  
Vol 15 (06) ◽  
pp. 1750062 ◽  
Author(s):  
Minchao Ye ◽  
Wenbin Zheng ◽  
Huijuan Lu ◽  
Xianting Zeng ◽  
Yuntao Qian
Keyword(s):  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Hyperspectral Image ◽  
Nonnegative Matrix ◽  
Subspace Learning ◽  
Manifold Regularization ◽  
Scene Classification ◽  
3D Wavelet Transform ◽  
Feature Subspace ◽  
Low Dimensional

Hyperspectral image (HSI) classification draws a lot of attentions in the past decades. The classical problem of HSI classification mainly focuses on a single HSI scene. In recent years, cross-scene classification becomes a new problem, which deals with the classification models that can be applied across different but highly related HSI scenes sharing common land cover classes. This paper presents a cross-scene classification framework combining spectral–spatial feature extraction and manifold-constrained feature subspace learning. In this framework, spectral–spatial feature extraction is completed using three-dimensional (3D) wavelet transform while manifold-constrained feature subspace learning is implemented via multitask nonnegative matrix factorization (MTNMF) with manifold regularization. In 3D wavelet transform, we drop some coefficients corresponding to high frequency in order to avoid data noise. In feature subspace learning, a common dictionary (basis) matrix is shared by different scenes during the nonnegative matrix factorization, indicating that the highly related scenes should share than same low-dimensional feature subspace. Furthermore, manifold regularization is applied to force the consistency across the scenes, i.e. all pixels representing the same land cover class should be similar in the low-dimensional feature subspace, though they may be drawn from different scenes. The experimental results show that the proposed method performs well in cross-scene HSI datasets.

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