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Fast subspace segmentation via Random Sample Probing
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DOI：10.1016/j.neucom.2018.08.054.png)
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摘要
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Subspace segmentation is to group a given set of n data points into multiple clusters, with each cluster corresponding to a subspace. Prevalent methods such as Sparse Subspace Clustering (SSC) and Low-Rank Representation (LRR) are effective in terms of segmentation accuracy, but computationally inefficient while applying to gigantic datasets where n is very large as they possess a complexity of O (n(3)). In this paper, we propose an iterative method called Random Sample Probing (RANSP). In each iteration, RANSP finds the members of one subspace by randomly choosing a data point (called seed) at first, and then using Ridge Regression (RR) to retrieve the other points that belong to the same subspace as the seed. Such a procedure is repeated until all points have been classified. RANSP has a computational complexity of O(n) and can therefore handle large-scale datasets. Experiments on synthetic and real datasets confirm the effectiveness and efficiency of RANSP. (c) 2018 Elsevier B.V. All rights reserved.
Keyword:
Clustering
Subspace segmentation
Large-scale
Random Sample Probing
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6.5
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2.5W
被引数:
6.5W
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