Matrix computations (3rd ed.)
An eigenspace update algorithm for image analysis
Graphical Models and Image Processing
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Random projection in dimensionality reduction: applications to image and text data
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Incremental Singular Value Decomposition of Uncertain Data with Missing Values
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
A Min-max Cut Algorithm for Graph Partitioning and Data Clustering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Database-friendly random projections: Johnson-Lindenstrauss with binary coins
Journal of Computer and System Sciences - Special issu on PODS 2001
Weighted and Robust Incremental Method for Subspace Learning
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Experiments with random projections for machine learning
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Spectral Grouping Using the Nyström Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Updating the singular value decomposition
Journal of Computational and Applied Mathematics
On the Nyström Method for Approximating a Gram Matrix for Improved Kernel-Based Learning
The Journal of Machine Learning Research
A tutorial on spectral clustering
Statistics and Computing
Incremental and robust learning of subspace representations
Image and Vision Computing
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Fast spectral learning using Lanczos eigenspace projections
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Approximate kernel k-means: solution to large scale kernel clustering
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Active spectral clustering via iterative uncertainty reduction
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
p-PIC: Parallel power iteration clustering for big data
Journal of Parallel and Distributed Computing
Weighted Fuzzy-Possibilistic C-Means Over Large Data Sets
International Journal of Data Warehousing and Mining
Large-scale spectral clustering on graphs
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Multi-view K-means clustering on big data
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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This paper proposes a fast spectral clustering method for large-scale data. In the present method, random projection and random sampling techniques are adopted for reducing the data dimensionality and cardinality. The computation time of the present method is quasi-linear with respect to the data cardinality. The clustering result can be updated with a small computational cost when data samples or random samples are appended or removed.