Sparse subspace clustering (SSC) is a state-of-the-art method for clustering high-dimensional data points lying in a union of low-dimensional subspaces. However, while l(1) optimization-based SSC algorithms suffer fro...
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Sparse subspace clustering (SSC) is a state-of-the-art method for clustering high-dimensional data points lying in a union of low-dimensional subspaces. However, while l(1) optimization-based SSC algorithms suffer from high computational complexity, other variants of SSC, such as orthogonal-matching-pursuit-based SSC (OMP-SSC), lose clustering accuracy in pursuit of improving time efficiency. In this letter, we propose a novel active OMP-SSC, which improves clustering accuracy of OMP-SSCby adaptively up-dating data points and randomly dropping data points in the OMP process, while still enjoying the low computational complexity of greedy pursuit algorithms. We provide heuristic analysis of our approach and explain how these two active steps achieve a better tradeoff between connectivity and separation. Numerical results on both synthetic data and real-world data validate our analyses and show the advantages of the proposed active algorithm.
In this paper, an efficient radio transmitter identification method is proposed for identifying radio transmitters. The square integral bispectra (SIB) transformation is firstly utilized to extract the features from t...
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ISBN:
(纸本)9781509037100
In this paper, an efficient radio transmitter identification method is proposed for identifying radio transmitters. The square integral bispectra (SIB) transformation is firstly utilized to extract the features from the raw signal data of radio transmitters, which recasts the problem of communication transmitter identification into the form of measuring similarity between points in its metric space. Then we use the collaborative representation framework as a platform to develop a novel classification model, correntropy-based collaborative representation classifier (CECRC), for identifying different radio transmitters according to the similarity between the points in the SIB feature space. Extensive experimental results on real-world data sets demonstrate the effectiveness of our proposed method.
In this paper, an efficient radio transmitter identification method is proposed for identifying radio transmitters. The square integral bispectra (SIB) transformation is firstly utilized to extract the features from t...
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ISBN:
(纸本)9781509037117
In this paper, an efficient radio transmitter identification method is proposed for identifying radio transmitters. The square integral bispectra (SIB) transformation is firstly utilized to extract the features from the raw signal data of radio transmitters, which recasts the problem of communication transmitter identification into the form of measuring similarity between points in its metric space. Then we use the collaborative representation framework as a platform to develop a novel classification model, correntropy-based collaborative representation classifier (CECRC), for identifying different radio transmitters according to the similarity between the points in the SIB feature space. Extensive experimental results on real-world data sets demonstrate the effectiveness of our proposed method.
The address non-transparency of NAT (Network Address Translation) device is a great challenge for managing and monitoring the network. Now a lot of problems exist in various detection technologies for obtaining the nu...
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