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Sparse Subspace Clustering: Algorithm, Theory, and Applications.
We propose and study an algorithm, called Sparse Subspace Clustering, to cluster high-dimensional data points that lie in a union of low-dimensional subspaces. The key idea is that, among infinitelyExpand
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Sparse subspace clustering
TLDR
We propose a method based on sparse representation (SR) to cluster data drawn from multiple low-dimensional linear or affine subspaces embedded in a high-dimensional space. Expand
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Sparse Subspace Clustering: Algorithm, Theory, and Applications
  • E. Elhamifar, R. Vidal
  • Computer Science, Mathematics
  • IEEE Transactions on Pattern Analysis and Machine…
  • 5 March 2012
TLDR
In this paper, we propose and study an algorithm, called sparse subspace clustering, to cluster data points that lie in a union of low-dimensional subspaces. Expand
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Sparse subspace clustering
We propose a method based on sparse representation (SR) to cluster data drawn from multiple low-dimensional linear or affine subspaces embedded in a high-dimensional space. Our method is based on theExpand
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See all by looking at a few: Sparse modeling for finding representative objects
TLDR
We consider the problem of finding a few representatives for a dataset, i.e., a subset of data points that efficiently describes the entire dataset. Expand
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Sparse Manifold Clustering and Embedding
TLDR
We propose an algorithm called Sparse Manifold Clustering and Embedding for simultaneous clustering and dimensionality reduction of data lying in multiple nonlinear manifolds. Expand
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Robust Subspace Clustering
TLDR
This paper introduces an algorithm inspired by sparse subspace clustering (SSC) (25) to cluster noisy data, and develops some novel theory demonstrating its correctness. Expand
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Robust classification using structured sparse representation
TLDR
In many problems in computer vision, data in multiple classes lie in multiple low-dimensional subspaces of a high-dimensional ambient space. Expand
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Energy Disaggregation via Learning Powerlets and Sparse Coding
TLDR
We propose a new supervised algorithm, which in the learning stage, automatically extracts signature consumption patterns of each device by modeling the device as a mixture of dynamical systems. Expand
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Dissimilarity-Based Sparse Subset Selection
TLDR
Finding an informative subset of a large collection of data points or models is at the center of many problems in computer vision, recommender systems, bio/health informatics as well as image and natural language processing. Expand
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