On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems
Theoretical Computer Science
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Segmentation Using Eigenvectors: A Unifying View
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Document Clustering Using Locality Preserving Indexing
IEEE Transactions on Knowledge and Data Engineering
Learning Spectral Clustering, With Application To Speech Separation
The Journal of Machine Learning Research
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
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In recent years, clustering techniques have become a useful tool in exploring data structures and have been employed in a broad range of applications. In this paper we derive a novel image clustering approach based on a sparse representation model, which assumes that each instance can be reconstructed by the sparse linear combination of other instances. Our method characterizes the graph adjacency structure and graph weights by sparse linear coefficients computed by solving ℓ1-minimization. Spectral clustering algorithm using these coefficients as graph weight matrix is then used to discover the cluster structure. Experiments confirmed the effectiveness of our approach.