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Spectral identification of networks using sparse measurements [article]

A. Mauroy, J. Hendrickx
2016 arXiv   pre-print
In contrast to classical identification of full graph topology, we focus on the identification of the spectral graph-theoretic properties of the network, a framework that we call spectral network identification  ...  Our framework provides efficient numerical methods to infer global information on the network from sparse local measurements at a few nodes.  ...  What does spectral information reveal about the graph? In contrast to classical network identification, spectral network identification only requires sparse measurements in the network.  ... 
arXiv:1601.04364v2 fatcat:rqxcs3vvnncwhmp7klhmy5fdye

Identifying Complex Brain Networks Using Penalized Regression Methods

Eduardo Martínez-Montes, Mayrim Vega-Hernández, José M. Sánchez-Bornot, Pedro A. Valdés-Sosa
2008 Journal of biological physics (Print)  
The recorded electrical activity of complex brain networks through the EEG reflects their intrinsic spatial, temporal and spectral properties.  ...  The use of appropriate constraints through non-convex penalties allowed three types of inverse solutions (Loreta, Lasso Fusion and ENet L) to spatially localize networks in agreement with previous studies  ...  Acknowledgments The authors thank Mark Cohen and Jhoanna Pérez-Hidalgo-Gato for kindly providing the data of the resting EEG and face identification experiment used in this study.  ... 
doi:10.1007/s10867-008-9077-0 pmid:19669480 pmcid:PMC2585631 fatcat:ffzk5hjveve6renuxvcqki7omq

Predicting and Identifying Missing Node Information in Social Networks

Ron Eyal, Avi Rosenfeld, Sigal Sina, Sarit Kraus
2013 ACM Transactions on Knowledge Discovery from Data  
Towards solving this problem, we present the MISC Algorithm (Missing node Identification by Spectral Clustering), an approach based on a spectral clustering algorithm, combined with nodes' pairwise affinity  ...  In addition, this paper also presents R-MISC which uses a sparse matrix representation, efficient algorithms for calculating the nodes' pairwise affinity and a proprietary dimension reduction technique  ...  However, the affinity matrix itself may not be sparse in some cases, depending on the affinity measure used.  ... 
doi:10.1145/2536775 fatcat:m35kzk7435enznarlyr7wgk7hy

A LITERATURE REVIEW ON HYPERSPECTRAL IMAGE DENOISING

V Chandrasekhar, Shaik Taj Mahaboob
2020 International Journal of Engineering Applied Sciences and Technology  
Sparse representation etc. while eliminating this noise using various techniques, we can apply for many problems like target detection, material identification on the earth's surface, and agriculture field  ...  These consist of two types of domains. They were spatial domain and spectral domain. Hyperspectral image contains very much contamination while capturing from a spectral camera.  ...  Sparse representation: Sparse representation is a technique that is using to DE-noise the hyperspectral images. It is used to convert high dimensional spectral into low dimensional [6] .  ... 
doi:10.33564/ijeast.2020.v05i07.038 fatcat:dajc2qm53bdyjjer2h2754fqla

Exact topology identification of large-scale interconnected dynamical systems from compressive observations

Borhan M. Sanandaji, Tyrone L. Vincent, Michael B. Wakin
2011 Proceedings of the 2011 American Control Conference  
The main technical novelty of our approach is in casting the identification problem as the recovery of a block-sparse signal x ∈ R N from the measurements b = Ax ∈ R M with M < N , where the measurement  ...  We use the term compressive observations in the case when there is a limited number of measurements available and thus the resulting inverse problem is highly underdetermined.  ...  All authors are with Division of Engineering, Colorado School of Mines, Golden, CO 80401, USA. Email:{bmolazem,tvincent,mwakin}@mines.edu.  ... 
doi:10.1109/acc.2011.5990982 fatcat:eq2fvz5igvew7ngpqt5xyddq2u

Robustness Analysis of Neural Networks with an Application to System Identification

K. Krishnakumar, K. Nishita
1999 Journal of Guidance Control and Dynamics  
A system identification problem is used to show the benefit of using sparse networks.  ...  Another type of network that has received some attention is the fully forward connected network.”> Using empirical results, KrishnaKumar’ has shown that if this type of network is made sparse, the networks  ... 
doi:10.2514/2.4437 fatcat:2yws34n44vaapgj54hmj4le6si

Dictionary pruning in sparse unmixing of hyperspectral data

Marian-Daniel Iordache, Jose M. Bioucas-Dias, Antonio Plaza
2012 2012 4th Workshop on Hyperspectral Image and Signal Processing (WHISPERS)  
When hyperspectral unmixing relies on the use of spectral libraries (dictionaries of pure spectra), the sparse regression problem to be solved is severely ill-conditioned and time-consuming.  ...  decrease the running time of the sparse unmixing algorithm.  ...  We use this measure instead of the classical root mean square error (RMSE) [9] as it gives more information regarding the power of the error in relation with the power of the signal.  ... 
doi:10.1109/whispers.2012.6874329 dblp:conf/whispers/IordacheBP12 fatcat:ouy3zhj2nrccvfjltvasixwswa

Network topology identification from imperfect spectral templates

Santiago Segarra, Antonio G. Marques, Gonzalo Mateos, Alejandro Ribeiro
2016 2016 50th Asilomar Conference on Signals, Systems and Computers  
eigenvectorsV of given network as noisy templatesEx: Infer contact between amino-acid residues in BPT1 BOVIN ⇒ Use mutual information of amino-acid covariation as input Recovery error Network 1 Network  ...  with increasing number of observed signals Error (right) decreases with increasing nr. of spectral templates Performance comparisons Comparison with graphical lasso and sparse correlation methods  ...  Our GSP approach to network topology inference ⇒ Two step approach: i) Obtain V; ii) Estimate S given V How to obtain the spectral templates V ⇒ Based on covariance of diffused signals ⇒ Other sources:  ... 
doi:10.1109/acssc.2016.7869620 dblp:conf/acssc/SegarraMMR16 fatcat:5xi22uedu5fw5fcv4i66uoh6ki

Endoscopic Depth Measurement and Super-Spectral-Resolution Imaging [article]

Jianyu Lin, Neil T. Clancy, Yang Hu, Ji Qi, Taran Tatla, Danail Stoyanov, Lena Maier-Hein, Daniel S. Elson
2017 arXiv   pre-print
We report an optical probe based system to combine sparse hyperspectral measurements and spectrally-encoded structured lighting (SL) for surface measurements.  ...  Furthermore, "super-spectral-resolution" was realized, whereby the RGB images and sparse hyperspectral data were integrated to recover dense pixel-level hyperspectral stacks, by using convolutional neural  ...  We refer to this method as super-spectral-resolution, i.e., achieving spatial super-resolution of sparse multispectral measurements by upscaling dense WL imag-es in the spectral domain.  ... 
arXiv:1706.06081v2 fatcat:v7evopclpzenlmqkbogrcojqhy

Community detection method based on mixed-norm sparse subspace clustering

Bo Tian, Weizi Li
2018 Neurocomputing  
Inspired by the sparse representation of subspace, each community in a given network can span a subspace in some similarity measure space.  ...  The formulation of the basis of subspaces is derived from the self-representation property of data by using SSC.  ...  To this end, a global sparse optimization program is adopted. Then, a spectral clustering framework is used to infer the clustering of the matrix of similarity measure.  ... 
doi:10.1016/j.neucom.2017.10.060 fatcat:ovv27q477jhtfpbo4ci6stfuwe

Genomic, Proteomic, and Metabolomic Data Integration Strategies

Kwanjeera Wanichthanarak, Johannes F. Fahrmann, Dmitry Grapov
2015 Biomarker Insights  
This review focuses on select methods and tools for the integration of metabolomic with genomic and proteomic data using a variety of approaches including biochemical pathway-, ontology-, network-, and  ...  Robust interpretation of experimental results measuring discreet biological domains remains a significant challenge in the face of complex biochemical regulation processes such as organismal versus tissue  ...  (rCCA), and sparse PLS discriminant analysis (sPLS-DA). 23 Weighted gene correlation network analysis (WGCNA) R package extends the concept of correlations to also include measures of graph topology  ... 
doi:10.4137/bmi.s29511 pmid:26396492 pmcid:PMC4562606 fatcat:w2kkb25ftjefbfycpu32z3e2qa

Endoscopic Depth Measurement and Super-Spectral-Resolution Imaging [chapter]

Jianyu Lin, Neil T. Clancy, Yang Hu, Ji Qi, Taran Tatla, Danail Stoyanov, Lena Maier-Hein, Daniel S. Elson
2017 Lecture Notes in Computer Science  
We report an optical probe based system to combine sparse hyperspectral measurements and spectrally-encoded structured lighting (SL) for surface measurements.  ...  Furthermore, super-spectral-resolution was realized, whereby the RGB images and sparse hyperspectral data were integrated to recover dense pixel-level hyperspectral stacks, by using convolutional neural  ...  We refer to this method as super-spectral-resolution, i.e., achieving spatial super-resolution of sparse multispectral measurements by upscaling dense WL imag-es in the spectral domain.  ... 
doi:10.1007/978-3-319-66185-8_5 fatcat:mnhnsdpyrvhcbj6zqrgjj4ii5q

Speaker Identification Using Sparsely Excited Speech Signals And Compressed Sensing

Eleni Karamichali
2010 Zenodo  
Publication in the conference proceedings of EUSIPCO, Aalborg, Denmark, 2010  ...  It is shown that the percentage of correct identification using compressed sensing theory can reach 80% on average using a number of measurements which are as low as half of the signal's samples.  ...  The performance measure used was the probability of correct identification of the speaker using (15) with n equal to 140 frames (2.8 seconds), and averaged over all 12 speakers.  ... 
doi:10.5281/zenodo.42197 fatcat:6mlbzmfqdrakdozvqmwuemosje

Group-constrained sparse fMRI connectivity modeling for mild cognitive impairment identification

Chong-Yaw Wee, Pew-Thian Yap, Daoqiang Zhang, Lihong Wang, Dinggang Shen
2013 Brain Structure and Function  
To this end, we formulate the R-fMRI time series of each region of interest (ROI) as a linear representation of time series of other ROIs to infer sparse connectivity networks that are topologically identical  ...  Specifically, l 1 -norm is imposed on each subject to filter out spurious or insignificant connections to produce sparse networks.  ...  Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904).  ... 
doi:10.1007/s00429-013-0524-8 pmid:23468090 pmcid:PMC3710527 fatcat:5mkoejwwvvfc5p7o3iowww6i7m

Titles

2019 2019 4th International Conference on Pattern Recognition and Image Analysis (IPRIA)  
Three-stream Very Deep Neural Network for Video Action Recognition Unsupervised Hyperspectral Target Detection Using Spectral Residual of Deep Autoencoder Networks Towards Information Theoretic Measurement  ...  A Memory-Based Collaborative Filtering Recommender System Using Social Ties Splicing localization in Tampered Blurred Images Epileptic Seizure Prediction Using Spectral Entropy-Based Features of EEG Automatic  ... 
doi:10.1109/pria.2019.8785061 fatcat:e43b5ycj2jfybk4ozrvqzhsk4a
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