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We consider the problem of clustering noisy high-dimensional data points into a union of low-dimensional subspaces and a set of outliers. The number of subspaces, their dimensions, and their orientati...
Greedy Feature Selection for Subspace Clustering
Subspace clustering unions of subspaces hybrid linear models sparse ap-proximation structured sparsity nearest neighbors low-rank approximation
2013/5/2
Unions of subspaces are powerful nonlinear signal models for collections of high-dimensional data. However, existing methods that exploit this structure require that the subspaces the signals of inter...
Subspace Clustering via Thresholding and Spectral Clustering
Subspace Clustering Thresholding Spectral Clustering
2013/5/2
We consider the problem of clustering a set of high-dimensional data points into sets of low-dimensional linear subspaces. The number of subspaces, their dimensions, and their orientations are unknown...
Model-based subspace clustering
COSA Dirichlet process mixture model nonparametric Bayes unsupervised learning variable selection
2009/9/21
We discuss a model-based approach to identifying clusters of objects
based on subsets of attributes, so that the attributes that distinguish a cluster
from the rest of the population may depend on t...