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Constructing a Nonnegative Low-Rank and Sparse Graph With Data-Adaptive Features

Liansheng Zhuang, Shenghua Gao, Jinhui Tang, Jingjing Wang, Zhouchen Lin, Yi Ma, Nenghai Yu
<span title="">2015</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/dhlhr4jqkbcmdbua2ca45o7kru" style="color: black;">IEEE Transactions on Image Processing</a> </i> &nbsp;
First, we propose to build a nonnegative low-rank and sparse (referred to as NNLRS) graph for the given data representation.  ...  Index Terms-Graph Construction, low-rank and sparse representation, semi-supervised learning, data embedding.  ...  NONNEGATIVE LOW-RANK AND SPARSE GRAPHS A.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tip.2015.2441632">doi:10.1109/tip.2015.2441632</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/26057712">pmid:26057712</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jhe7svay5zgpnf4bqgssh7xnq4">fatcat:jhe7svay5zgpnf4bqgssh7xnq4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20161203112817/http://www.cis.pku.edu.cn/faculty/vision/zlin/Publications/2015-TIP-NNLRS-Graph.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/37/37/373785462b77c813192ba4e443d2f2f34cdec27b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tip.2015.2441632"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Constructing a Non-Negative Low Rank and Sparse Graph with Data-Adaptive Features [article]

Liansheng Zhuang, Shenghua Gao, Jinhui Tang, Jingjing Wang, Zhouchen Lin, Yi Ma
<span title="2014-09-03">2014</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Specifically, the weights of edges in the graph are obtained by seeking a nonnegative low-rank and sparse matrix that represents each data sample as a linear combination of others.  ...  Firstly, we propose to build a non-negative low-rank and sparse (referred to as NNLRS) graph for the given data representation.  ...  NONNEGATIVE LOW-RANK AND SPARSE GRAPHS A.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1409.0964v1">arXiv:1409.0964v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rxgmo3denbfyvfmbjtxinwyzdu">fatcat:rxgmo3denbfyvfmbjtxinwyzdu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200908000040/https://arxiv.org/pdf/1409.0964v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/9b/b0/9bb093e26c26a77c49fe9f1667044c42aaba6573.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1409.0964v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Gene Feature Extraction Based on Nonnegative Dual Graph Regularized Latent Low-Rank Representation

Guoliang Yang, Zhengwei Hu
<span title="">2017</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/icbhosh775h7bgzgot6avm3cua" style="color: black;">BioMed Research International</a> </i> &nbsp;
Aiming at the problem of gene expression profile's high redundancy and heavy noise, a new feature extraction model based on nonnegative dual graph regularized latent low-rank representation (NNDGLLRR)  ...  is presented on the basis of latent low-rank representation (Lat-LRR).  ...  Acknowledgments This paper is supported by the National Nature Science Foundation of China (nos. 51365017 and 61305019) and the Science and Technology Project of Jiangxi Province Education Department (  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2017/1096028">doi:10.1155/2017/1096028</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/28466003">pmid:28466003</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC5390636/">pmcid:PMC5390636</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ctf6ns6n6zdgzc37dtmwrz5t6u">fatcat:ctf6ns6n6zdgzc37dtmwrz5t6u</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190304121433/http://pdfs.semanticscholar.org/bd73/527b1e3b042d9c1b01926b3fd90982c01377.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/bd/73/bd73527b1e3b042d9c1b01926b3fd90982c01377.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2017/1096028"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> hindawi.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5390636" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Low‐rank nonnegative sparse representation and local preservation‐based matrix regression for supervised image feature selection

Xingyu Zhu, Xiuhong Chen
<span title="2021-06-15">2021</span> <i title="Institution of Engineering and Technology (IET)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/dscocsarvbb5boe7eixzypaetu" style="color: black;">IET Image Processing</a> </i> &nbsp;
To this end, we propose a lowrank nonnegative sparse representation and local preserving matrix regression (LNSRLP-MR) model for image feature selection.  ...  To capture the global structure and discriminative information of the training images and reduce the effect of heterogeneous data and noises, we impose the low-rank constraint on the self-representation  ...  CONCLUSION In this paper, we use the learned nonnegative coefficients in the self-representation of image data to adaptively construct an affinity graph, and then propose a new sparse matrix regression  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1049/ipr2.12281">doi:10.1049/ipr2.12281</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/tt6co6nvpzfqfj7yr4xambc7iu">fatcat:tt6co6nvpzfqfj7yr4xambc7iu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210715055226/https://ietresearch.onlinelibrary.wiley.com/doi/pdfdirect/10.1049/ipr2.12281" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/55/a4/55a4004ce407a7baff1bbc16505617d77a6d50e3.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1049/ipr2.12281"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Sparse dual graph-regularized deep nonnegative matrix factorization for image clustering

Weiyu Guo
<span title="">2021</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
In this paper, we propose a novel approach to address the above two problems, referred to as Sparse Dual Graph-regularized Deep Nonnegative Matrix Factorization (SDG Deep NMF), which can learn sparse and  ...  INDEX TERMS Deep nonnegative matrix factorization, dual graph regularization, sparse constraints, image clustering. 39926 This work is licensed under a Creative Commons Attribution 4.0 License.  ...  (CNMF) [15] , Graph regularized and Sparse Nonnegative Matrix Factorization with hard Constraints (GSNMFC) [16] , and regularized Nonnegative Matrix Factorization with Adaptive Neighbors (NMFAN) [17  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2021.3064631">doi:10.1109/access.2021.3064631</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gfui7rogzbffrfpls23nmsqx3y">fatcat:gfui7rogzbffrfpls23nmsqx3y</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210717201742/https://ieeexplore.ieee.org/ielx7/6287639/9312710/09373393.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/13/3b/133ba642ac8b000be282933996ce78df8627a5c8.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2021.3064631"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> ieee.com </button> </a>

Robust Graph Learning From Noisy Data

Zhao Kang, Haiqi Pan, Steven C. H. Hoi, Zenglin Xu
<span title="2019-01-08">2019</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/snhjqrgxbff5teva5lfasxmfr4" style="color: black;">IEEE Transactions on Cybernetics</a> </i> &nbsp;
In this paper, we propose a novel robust graph learning scheme to learn reliable graphs from the real-world noisy data by adaptively removing noise and errors in the raw data.  ...  exploiting the graph smoothness assumption and 2) improved graph construction by exploiting clean data recovered by RPCA.  ...  Specifically, we decompose the original data into a low-rank matrix D ("clean data") and a sparse matrix E ("noise/errors").  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tcyb.2018.2887094">doi:10.1109/tcyb.2018.2887094</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/30629527">pmid:30629527</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qkvy5szpa5ajhj7ugigsg5oqgq">fatcat:qkvy5szpa5ajhj7ugigsg5oqgq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200709061117/https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=6136&amp;context=sis_research" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ac/44/ac4441946795b8db7fe7c684017c76bb92bee2f3.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tcyb.2018.2887094"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

A Survey on Concept Factorization: From Shallow to Deep Representation Learning [article]

Zhao Zhang, Yan Zhang, Mingliang Xu, Li Zhang, Yi Yang, Shuicheng Yan
<span title="2021-01-31">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
As a relatively new paradigm for representation learning, Concept Factorization (CF) has attracted a great deal of interests in the areas of machine learning and data mining for over a decade.  ...  The quality of learned features by representation learning determines the performance of learning algorithms and the related application tasks (such as high-dimensional data clustering).  ...  ACKNOWLEDGMENT This work is partially supported by the National Natural Science Foundation of China (61672365) and the Fundamental Research Funds for the Central Universities of China (JZ2019H-  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2007.15840v3">arXiv:2007.15840v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ahun2mogmfapxe4mqhqlsakyku">fatcat:ahun2mogmfapxe4mqhqlsakyku</a> </span>
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Adaptive Graph Regularized Low–Rank Matrix Factorization With Noise and Outliers for Clustering

Min Zhao, Jinglei Liu
<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
INDEX TERMS Adaptive graph regularization, clustering, 2,1 , noise and outliers, augmented lagrangian method. , IEEE Access J.Liu et al.: Adaptive Graph Regularized Low-Rank Matrix Factorization with Noise  ...  In this paper, a robust clustering model with adaptive graph regularization (RCAG) is proposed, on which, sparse error matrix is introduced to express sparse noise, such as impulse noise, dead line, stripes  ...  By which, the adaptive graph regularization, sparse error matrix and nonnegative low-rank matrix decomposition are integrated into a unified objective function shown by Equation 3 . (2) In order to get  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.3025096">doi:10.1109/access.2020.3025096</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/l73g7jiee5davfurtglnx2i4qi">fatcat:l73g7jiee5davfurtglnx2i4qi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201108174448/https://ieeexplore.ieee.org/ielx7/6287639/6514899/09201009.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/92/39/9239e0401f6e8ae19b426404647f0e3945c1e259.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.3025096"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> ieee.com </button> </a>

Correlation Preserving Sparse Coding Over Multi-level Dictionaries for Image Denoising [article]

Rui Chen, Huizhu Jia, Xiaodong Xie, Wen Gao
<span title="2016-12-23">2016</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In particular, edge weights in the graph are obtained by seeking a nonnegative low-rank construction.  ...  Specifically, a graph-based regularizer is built for preserving the global similarity correlations, which can adaptively capture both the geometrical structures and discriminative features of textured  ...  Given a set of data point , we can construct a nearest neighbor graph with n vertices denoting all data points.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1612.08049v1">arXiv:1612.08049v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4ip2gs6i2fctjbdiqfdioqrz24">fatcat:4ip2gs6i2fctjbdiqfdioqrz24</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200826220913/https://arxiv.org/ftp/arxiv/papers/1612/1612.08049.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/3d/e8/3de8f98457c08915299cc60d1c230d1c32a3163a.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1612.08049v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Subspace structural constraint-based discriminative feature learning via nonnegative low rank representation

Ao Li, Xin Liu, Yanbing Wang, Deyun Chen, Kezheng Lin, Guanglu Sun, Hailong Jiang, Kim Han Thung
<span title="2019-05-07">2019</span> <i title="Public Library of Science (PLoS)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/s3gm7274mfe6fcs7e3jterqlri" style="color: black;">PLoS ONE</a> </i> &nbsp;
in the proposed learning model for facilitating data adaptation and robustness.  ...  In this paper, we propose a robust feature subspace learning approach based on a low-rank representation.  ...  Acknowledgments The authors are grateful to the editor and anonymous reviewers for their valuable review comments on our work.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pone.0215450">doi:10.1371/journal.pone.0215450</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/31063497">pmid:31063497</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6504107/">pmcid:PMC6504107</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jkmjgu5j5najnpvh6777je3ukm">fatcat:jkmjgu5j5najnpvh6777je3ukm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191214051119/https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0215450&amp;type=printable" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/42/c9/42c90f99d2f1bf5a0fc34a8d95765a5080027825.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pone.0215450"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> plos.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6504107" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Beyond Low-Rank Representations: Orthogonal Clustering Basis Reconstruction with Optimized Graph Structure for Multi-view Spectral Clustering [article]

Yang Wang, Lin Wu
<span title="2018-03-22">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
low-rank self-expressive data similarity embedded in high-dimensional space, to yield a better graph partition than their single-view counterparts.  ...  In this paper we revisit it with a fundamentally different perspective by discovering LRR as essentially a latent clustered orthogonal projection based representation winged with an optimized local graph  ...  case of a generalized Low-Rank projection, to map feature representation to a low-dimensional space to reconstruct X i with minimum error.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1708.02288v4">arXiv:1708.02288v4</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/k2jk4ihedjchdn7riy5pvcfzrm">fatcat:k2jk4ihedjchdn7riy5pvcfzrm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200823032135/https://arxiv.org/pdf/1708.02288v4.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/fe/23/fe2301ab0a73a367950d507e9a5a72fe5166947c.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1708.02288v4" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Learning manifold to regularize nonnegative matrix factorization [article]

Jim Jing-Yan Wang, Xin Gao
<span title="2014-10-03">2014</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Multiple graph learning is proposed to solve the problem of graph model selection, adaptive graph learning via feature selection is proposed to solve the problem of constructing a graph from noisy features  ...  However, how to construct an optimal graph to present the manifold prop- erly remains a difficultproblem due to the graph modelselection, noisy features, and nonlinear distributed data.  ...  Given a matrix of nonnegative data, where each column is a data sample, NMF tries to represent it as a product of two low rank nonnegative matrices, i.e., a basis matrix and a coefficient matrix.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1410.2191v1">arXiv:1410.2191v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/j2qf2oymzjgdbk4azccg4yercq">fatcat:j2qf2oymzjgdbk4azccg4yercq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200824004013/https://arxiv.org/pdf/1410.2191v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/57/4b/574b223eb9a88967a524ca10aeaf2806dc649225.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1410.2191v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Self-Representative Manifold Concept Factorization with Adaptive Neighbors for Clustering

Sihan Ma, Lefei Zhang, Wenbin Hu, Yipeng Zhang, Jia Wu, Xuelong Li
<span title="">2018</span> <i title="International Joint Conferences on Artificial Intelligence Organization"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/vfwwmrihanevtjbbkti2kc3nke" style="color: black;">Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence</a> </i> &nbsp;
data with itself as a dictionary.  ...  To further improve the clustering performance, we propose a novel manifold concept factorization model with adaptive neighbor structure to learn a better affinity matrix and clustering indicator matrix  ...  In our proposed model, we construct the affinity matrix with adaptive neighbors based on the renewable coefficient matrix and learn a sparse data representation simultaneously.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.24963/ijcai.2018/352">doi:10.24963/ijcai.2018/352</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/ijcai/MaZHZWL18.html">dblp:conf/ijcai/MaZHZWL18</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/h7kbcamq6zfi7csoaud7cevcgq">fatcat:h7kbcamq6zfi7csoaud7cevcgq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190429115950/https://www.ijcai.org/proceedings/2018/0352.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/7e/24/7e249d6f3c117e41b04111598805dd62b03e60c4.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.24963/ijcai.2018/352"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Non-Negative Symmetric Low-Rank Representation Graph Regularized Method for Cancer Clustering Based on Score Function

Conghai Lu, Juan Wang, Jinxing Liu, Chunhou Zheng, Xiangzhen Kong, Xiaofeng Zhang
<span title="2020-01-22">2020</span> <i title="Frontiers Media SA"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/r7trx2kj6je5jhtaoy3rztibgy" style="color: black;">Frontiers in Genetics</a> </i> &nbsp;
In this paper, we propose a novel integrated framework for cancer clustering known as the non-negative symmetric low-rank representation with graph regularization based on score function (NSLRG-S).  ...  Second, we construct the Score function based on the lowest rank matrix to weight all of the features of the gene expression data and calculate the score of each feature.  ...  We have uploaded scripts and examples on GitHub to adhere standards for reproducibility. The URL is https://github.com/ guoguoguolu/NSLRG-S-method-scripts-and-example-files.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fgene.2019.01353">doi:10.3389/fgene.2019.01353</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/32038712">pmid:32038712</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6987458/">pmcid:PMC6987458</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xabajg3z4zgjnprjy75dxrwcla">fatcat:xabajg3z4zgjnprjy75dxrwcla</a> </span>
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Low-rank graph optimization for multi-view dimensionality reduction

Youcheng Qian, Xueyan Yin, Jun Kong, Jianzhong Wang, Wei Gao, Denis Horvath
<span title="2019-12-18">2019</span> <i title="Public Library of Science (PLoS)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/s3gm7274mfe6fcs7e3jterqlri" style="color: black;">PLoS ONE</a> </i> &nbsp;
First, we construct a low-rank shared matrix and a sparse error matrix from the graph that corresponds to each view for capturing potential noise.  ...  In this paper, we propose a novel algorithm, namely, Low-Rank Graph Optimization for Multi-View Dimensionality Reduction (LRGO-MVDR), that overcomes these limitations.  ...  With reasonable low-rank and sparse constraints, the algorithm can effectively deal with the noise in the multi-view input data.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pone.0225987">doi:10.1371/journal.pone.0225987</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/31851696">pmid:31851696</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6919611/">pmcid:PMC6919611</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6vd7duruj5dite4gtmglsm3rm4">fatcat:6vd7duruj5dite4gtmglsm3rm4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191219050207/https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0225987&amp;type=printable" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/dc/fb/dcfbb9da9483ee34d63f538dab1550e0c3c5e191.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pone.0225987"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> plos.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6919611" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>
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