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

2020 ◽  
Vol 16 (4) ◽  
pp. e1007791
Author(s):  
Shreya Saxena ◽  
Ian Kinsella ◽  
Simon Musall ◽  
Sharon H. Kim ◽  
Jozsef Meszaros ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Calcium Imaging ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Imaging Data

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

scholarly journals Sparse modeling of EELS and EDX spectral imaging data by nonnegative matrix factorization

2016 ◽  
Vol 170 ◽  
pp. 43-59 ◽  
Author(s):  
Motoki Shiga ◽  
Kazuyoshi Tatsumi ◽  
Shunsuke Muto ◽  
Koji Tsuda ◽  
Yuta Yamamoto ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Spectral Imaging ◽  
Imaging Data ◽  
Sparse Modeling

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 EASE: EM-Assisted Source Extraction from calcium imaging data

2020 ◽  
Author(s):  
Pengcheng Zhou ◽  
Jacob Reimer ◽  
Ding Zhou ◽  
Amol Pasarkar ◽  
Ian Kinsella ◽  
...  
Keyword(s):  
Neural Network ◽  
Matrix Factorization ◽  
Calcium Imaging ◽  
Nonnegative Matrix ◽  
Automated Analysis ◽  
Current Approach ◽  
Imaging Data ◽  
Matching Problem ◽  
Factorization Problem

AbstractCombining two-photon calcium imaging (2PCI) and electron microscopy (EM) provides arguably the most powerful current approach for connecting function to structure in neural circuits. Recent years have seen dramatic advances in obtaining and processing CI and EM data separately. In addition, several joint CI-EM datasets (with CI performed in vivo, followed by EM reconstruction of the same volume) have been collected. However, no automated analysis tools yet exist that can match each signal extracted from the CI data to a cell segment extracted from EM; previous efforts have been largely manual and focused on analyzing calcium activity in cell bodies, neglecting potentially rich functional information from axons and dendrites. There are two major roadblocks to solving this matching problem: first, dense EM reconstruction extracts orders of magnitude more segments than are visible in the corresponding CI field of view, and second, due to optical constraints and non-uniform brightness of the calcium indicator in each cell, direct matching of EM and CI spatial components is nontrivial.In this work we develop a pipeline for fusing CI and densely-reconstructed EM data. We model the observed CI data using a constrained nonnegative matrix factorization (CNMF) framework, in which segments extracted from the EM reconstruction serve to initialize and constrain the spatial components of the matrix factorization. We develop an efficient iterative procedure for solving the resulting combined matching and matrix factorization problem and apply this procedure to joint CI-EM data from mouse visual cortex. The method recovers hundreds of dendritic components from the CI data, visible across multiple functional scans at different depths, matched with densely-reconstructed three-dimensional neural segments recovered from the EM volume. We publicly release the output of this analysis as a new gold standard dataset that can be used to score algorithms for demixing signals from 2PCI data. Finally, we show that this database can be exploited to (1) learn a mapping from 3d EM segmentations to predict the corresponding 2d spatial components estimated from CI data, and (2) train a neural network to denoise these estimated spatial components. This neural network denoiser is a stand-alone module that can be dropped in to enhance any existing 2PCI analysis pipeline.

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
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