Dual Graph Regularized Latent Low-Rank Representation for Subspace Clustering
暂无分享,去创建一个
Junbin Gao | Ming Yin | Qinfeng Shi | Zhouchen Lin | Yi Guo | Zhouchen Lin | Junbin Gao | Yi Guo | Ming Yin | Javen Qinfeng Shi
[1] Feng Li,et al. Random spatial subspace clustering , 2015, Knowl. Based Syst..
[2] Shuicheng Yan,et al. Neighborhood preserving embedding , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.
[3] Hyunsoo Kim,et al. Nonnegative Matrix Factorization Based on Alternating Nonnegativity Constrained Least Squares and Active Set Method , 2008, SIAM J. Matrix Anal. Appl..
[4] Quanquan Gu,et al. Co-clustering on manifolds , 2009, KDD.
[5] U. Feige,et al. Spectral Graph Theory , 2015 .
[6] Fei Wang,et al. Graph dual regularization non-negative matrix factorization for co-clustering , 2012, Pattern Recognit..
[7] Allen Y. Yang,et al. Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[8] Rama Chellappa,et al. Statistical analysis on Stiefel and Grassmann manifolds with applications in computer vision , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[9] Aggelos K. Katsaggelos,et al. Sparse Bayesian Methods for Low-Rank Matrix Estimation , 2011, IEEE Transactions on Signal Processing.
[10] Chun Chen,et al. Graph Regularized Sparse Coding for Image Representation , 2011, IEEE Transactions on Image Processing.
[11] Nathan Halko,et al. Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions , 2009, SIAM Rev..
[12] Xinlei Chen,et al. Large Scale Spectral Clustering with Landmark-Based Representation , 2011, AAAI.
[13] John Wright,et al. Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Matrices via Convex Optimization , 2009, NIPS.
[14] Arvind Ganesh,et al. Fast Convex Optimization Algorithms for Exact Recovery of a Corrupted Low-Rank Matrix , 2009 .
[15] Zhixun Su,et al. Linearized alternating direction method with parallel splitting and adaptive penalty for separable convex programs in machine learning , 2013, Machine Learning.
[16] J. Tenenbaum,et al. A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.
[17] Chao-Hong Chen,et al. Convergence Time Analysis of Particle Swarm Optimization Based on Particle Interaction , 2011, Adv. Artif. Intell..
[18] S. Yun,et al. An accelerated proximal gradient algorithm for nuclear norm regularized linear least squares problems , 2009 .
[19] Guillermo Sapiro,et al. Sparse Representation for Computer Vision and Pattern Recognition , 2010, Proceedings of the IEEE.
[20] Lei Zhang,et al. Sparse representation or collaborative representation: Which helps face recognition? , 2011, 2011 International Conference on Computer Vision.
[21] Nenghai Yu,et al. Non-negative low rank and sparse graph for semi-supervised learning , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[22] Yu-Chiang Frank Wang,et al. Low-rank matrix recovery with structural incoherence for robust face recognition , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[23] Liang-Tien Chia,et al. Laplacian Sparse Coding, Hypergraph Laplacian Sparse Coding, and Applications , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[24] Yulong Wang,et al. Graph-Regularized Low-Rank Representation for Destriping of Hyperspectral Images , 2013, IEEE Transactions on Geoscience and Remote Sensing.
[25] Stephen Lin,et al. Graph Embedding and Extensions: A General Framework for Dimensionality Reduction , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[26] Xiaojun Wu,et al. Graph Regularized Nonnegative Matrix Factorization for Data Representation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[27] Yong Yu,et al. Robust Subspace Segmentation by Low-Rank Representation , 2010, ICML.
[28] Yann LeCun,et al. Dimensionality Reduction by Learning an Invariant Mapping , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[29] Wotao Yin,et al. Alternating direction augmented Lagrangian methods for semidefinite programming , 2010, Math. Program. Comput..
[30] 丸山 徹. Convex Analysisの二,三の進展について , 1977 .
[31] Zhang Yi,et al. Scalable Sparse Subspace Clustering , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[32] Yuxiao Hu,et al. Face recognition using Laplacianfaces , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[33] Marc Teboulle,et al. A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems , 2009, SIAM J. Imaging Sci..
[34] Emmanuel J. Candès,et al. A Singular Value Thresholding Algorithm for Matrix Completion , 2008, SIAM J. Optim..
[35] Junbin Gao,et al. Robust face recognition via double low-rank matrix recovery for feature extraction , 2013, 2013 IEEE International Conference on Image Processing.
[36] G. Sapiro,et al. A collaborative framework for 3D alignment and classification of heterogeneous subvolumes in cryo-electron tomography. , 2013, Journal of structural biology.
[37] Nikos D. Sidiropoulos,et al. Co-clustering as multilinear decomposition with sparse latent factors , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[38] Michael Elad,et al. Compression of facial images using the K-SVD algorithm , 2008, J. Vis. Commun. Image Represent..
[39] Mikhail Belkin,et al. Towards a theoretical foundation for Laplacian-based manifold methods , 2005, J. Comput. Syst. Sci..
[40] Shuyuan Yang,et al. Low-rank representation with local constraint for graph construction , 2013, Neurocomputing.
[41] Zhenyue Zhang,et al. Low-Rank Matrix Approximation with Manifold Regularization , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[42] Stephen P. Boyd,et al. Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..
[43] Yong Yu,et al. Robust Recovery of Subspace Structures by Low-Rank Representation , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[44] Junbin Gao,et al. Transposed Low Rank Representation for Image Classification , 2012, 2012 International Conference on Digital Image Computing Techniques and Applications (DICTA).
[45] Qi Tian,et al. Image classification by non-negative sparse coding, low-rank and sparse decomposition , 2011, CVPR 2011.
[46] Junbin Gao,et al. Laplacian Regularized Low-Rank Representation and Its Applications , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[47] Inderjit S. Dhillon,et al. Co-clustering documents and words using bipartite spectral graph partitioning , 2001, KDD '01.
[48] Yi Ma,et al. Robust principal component analysis? , 2009, JACM.
[49] Shuicheng Yan,et al. Latent Low-Rank Representation for subspace segmentation and feature extraction , 2011, 2011 International Conference on Computer Vision.
[50] Zhixun Su,et al. Linearized Alternating Direction Method with Adaptive Penalty for Low-Rank Representation , 2011, NIPS.
[51] Mikhail Belkin,et al. Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering , 2001, NIPS.
[52] Inderjit S. Dhillon,et al. Information-theoretic co-clustering , 2003, KDD '03.
[53] Mikhail Belkin,et al. Laplacian Eigenmaps for Dimensionality Reduction and Data Representation , 2003, Neural Computation.
[54] Xiaoming Yuan,et al. Recovering Low-Rank and Sparse Components of Matrices from Incomplete and Noisy Observations , 2011, SIAM J. Optim..
[55] Yi Ma,et al. The Augmented Lagrange Multiplier Method for Exact Recovery of Corrupted Low-Rank Matrices , 2010, Journal of structural biology.
[56] S T Roweis,et al. Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.
[57] Hujun Bao,et al. Understanding the Power of Clause Learning , 2009, IJCAI.
[58] Hujun Bao,et al. Laplacian Regularized Gaussian Mixture Model for Data Clustering , 2011, IEEE Transactions on Knowledge and Data Engineering.