
Fast greedy algorithm for subspace clustering from corrupted and incomplete data
We describe the Fast Greedy Sparse Subspace Clustering (FGSSC) algorithm...
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Exact Subspace Segmentation and Outlier Detection by LowRank Representation
In this work, we address the following matrix recovery problem: suppose ...
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Sparse Subspace Clustering: Algorithm, Theory, and Applications
In many realworld problems, we are dealing with collections of highdim...
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Tensor Matched Subspace Detection
The problem of testing whether an incomplete tensor lies in a given tens...
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Tensor Matched KroneckerStructured Subspace Detection for Missing Information
We consider the problem of detecting whether a tensor signal having many...
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Multiple pattern classification by sparse subspace decomposition
A robust classification method is developed on the basis of sparse subsp...
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GLIMPS: A Greedy Mixed Integer Approach for Super Robust Matched Subspace Detection
Due to diverse nature of data acquisition and modern applications, many contemporary problems involve high dimensional datum ∈^$̣ whose entries often lie in a union of subspaces and the goal is to find out which entries ofmatch with a particular subspace, classically called matched subspace detection. Consequently, entries that match with one subspace are considered as inliers w.r.t the subspace while all other entries are considered as outliers. Proportion of outliers relative to each subspace varies based on the degree of coordinates from subspaces. This problem is a combinatorial NPhard in nature and has been immensely studied in recent years. Existing approaches can solve the problem when outliers are sparse. However, if outliers are abundant or in other words ifcontains coordinates from a fair amount of subspaces, this problem can't be solved with acceptable accuracy or within a reasonable amount of time. This paper proposes a twostage approach called Greedy Linear Integer Mixed Programmed Selector (GLIMPS) for this abundantoutliers setting, which combines a greedy algorithm and mixed integer formulation and can tolerate over 80% outliers, outperforming the stateoftheart.
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