Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Acquiring Linear Subspaces for Face Recognition under Variable Lighting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized Principal Component Analysis (GPCA)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combined central and subspace clustering for computer vision applications
ICML '06 Proceedings of the 23rd international conference on Machine learning
Multiframe Motion Segmentation with Missing Data Using PowerFactorization and GPCA
International Journal of Computer Vision
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Fast Algorithm for Edge-Preserving Variational Multichannel Image Restoration
SIAM Journal on Imaging Sciences
Interior-Point Method for Nuclear Norm Approximation with Application to System Identification
SIAM Journal on Matrix Analysis and Applications
Robust recovery of signals from a structured union of subspaces
IEEE Transactions on Information Theory
Decomposing background topics from keywords by principal component pursuit
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Robust Positive semidefinite L-Isomap Ensemble
Pattern Recognition Letters
Robust Low-Rank Subspace Segmentation with Semidefinite Guarantees
ICDMW '10 Proceedings of the 2010 IEEE International Conference on Data Mining Workshops
Clustering appearances of objects under varying illumination conditions
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Robust principal component analysis?
Journal of the ACM (JACM)
Fast density-weighted low-rank approximation spectral clustering
Data Mining and Knowledge Discovery
Integrating low-rank and group-sparse structures for robust multi-task learning
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Learning Spectral Embedding for Semi-supervised Clustering
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Graph dual regularization non-negative matrix factorization for co-clustering
Pattern Recognition
A closed form solution to robust subspace estimation and clustering
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Accelerated low-rank visual recovery by random projection
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Segmentation of Multivariate Mixed Data via Lossy Data Coding and Compression
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semi-supervised learning with mixed knowledge information
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Latent Low-Rank Representation for subspace segmentation and feature extraction
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Integrating Spectral Kernel Learning and Constraints in Semi-Supervised Classification
Neural Processing Letters
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In recent years, robust subspace clustering is an important unsupervised clustering problem in machine learning and computer vision communities. The recently proposed spectral clustering based approach, called low-rank representation (LRR), yields an optimal solution for the case of independent subspaces and partially corrupted data. However, it has to be solved iteratively and involves singular value decomposition (SVD) at each iteration, and then suffers from high computation cost of multiple SVDs. In this paper, we propose an efficient matrix tri-factorization (MTF) approach with a positive semidefinite (PSD) constraint to approximate the original nuclear norm minimization (NNM) problem and mitigate the computation cost of performing SVDs. Specially, we introduce a matrix tri-factorization idea into the original low-rank representation framework, and then convert it into a small scale matrix nuclear norm minimization problem. Finally, we establish an alternating direction method (ADM) based algorithm to efficiently solve the proposed problem. Experimental results on a variety of synthetic and real-world data sets validate the efficiency, robustness and effectiveness of the proposed MTF approach comparing with the state-of-the-art algorithms.