Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Subspace clustering refers to the task of nding a multi-subspace representation that best ts a collection of points taken from a high-dimensional space.  introduces an algorithm inspired by sparse subspace clustering (SSC) (25) to cluster noisy data, and develops some novel theory demonstrating its correctness. In particular, the theory uses ideas from geometric functional analysis to show that the algorithm can accurately recover the underlying subspaces under minimal requirements on their orientation, and on the number of samples per subspace. Synthetic as ll as real data experiments complement our theoretical study, illustrating our approach and demonstrating its eectiveness.
['Mahdi Soltanolkotabi', 'Ehsan Elhamifar', 'Emmanuel J. Candès']
I am writing a research paper on a idea Robust subspace clustering
[{"from": "human", "value": "I am writing a research paper on a idea Robust subspace clustering"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Subspace clustering refers to the task of nding a multi-subspace representation that best ts a collection of points taken from a high-dimensional space.  introduces an algorithm inspired by sparse subspace clustering (SSC) (25) to cluster noisy data, and develops some novel theory demonstrating its correctness. In particular, the theory uses ideas from geometric functional analysis to show that the algorithm can accurately recover the underlying subspaces under minimal requirements on their orientation, and on the number of samples per subspace. Synthetic as ll as real data experiments complement our theoretical study, illustrating our approach and demonstrating its eectiveness."}]