Jacobi--Davidson Style QR and QZ Algorithms for the Reduction of Matrix Pencils
SIAM Journal on Scientific Computing
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Think globally, fit locally: unsupervised learning of low dimensional manifolds
The Journal of Machine Learning Research
Multiclass Spectral Clustering
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Convex Optimization
Learning Spectral Clustering, With Application To Speech Separation
The Journal of Machine Learning Research
Label Propagation through Linear Neighborhoods
IEEE Transactions on Knowledge and Data Engineering
Clustering Via Local Regression
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
On Defining Partition Entropy by Inequalities
IEEE Transactions on Information Theory
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Motivated by the local reconstruction approach to discovering low dimensional structure in high dimensional data, we propose a novel clustering algorithm that effectively utilizes local reconstruction information. We obtain the local reconstruction weights by minimizing the reconstruction error between each data point and the reconstruction from its neighbors. An entropy regularization term is incorporated into the reconstruction objective function so that the smoothness of the reconstruction weights can be explicitly controlled. The reconstruction weights are then used to obtain the clustering result by employing spectral clustering techniques. Experimental results on a number of datasets demonstrate that our algorithm performs well relative to other approaches, which validate the effectiveness of our approach for clustering.