Binary Reduced Row Echelon Form Approach for Subspace Segmentation
نویسندگان
چکیده
This paper introduces a subspace segmentation and data clustering method for a set of data drawn from a union of subspaces. The proposed method works perfectly in absence of noise, i.e., it can find the number of subspaces, their dimensions, and an orthonormal basis for each subspace. The effect of noise on this approach depends on the noise level and relative positions of subspaces. We provide a performance analysis in presence of noise and outliers.
منابع مشابه
Reduced row echelon form and non-linear approximation for subspace segmentation and high-dimensional data clustering
Article history: Received 10 May 2012 Received in revised form 9 December 2013 Accepted 15 December 2013 Available online 17 December 2013 Communicated by Jared Tanner
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