Local information-based fast approximate spectral clustering

Cao, Jiangzhong; Chen, Pei; Dai, Qingyun; Ling, Wing‐Kuen

doi:10.1016/j.patrec.2013.11.005

Cited by 25 publications

(20 citation statements)

References 17 publications

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“…Select survivor according to the wheel selection rule [23]; (6) End for (7) For all ∈ (0, 1) to max do (8) For all = 1 to do (9) Select randomly two individuals and calculate the crossover probability according to Eq. (12); (10) Generate a random floating-point number , ∈ (0, 1); (11) If ≤ then (12) Perform arithmetic crossover operation [24]; (13) End if (14) End for (15) For all = 1 to do (16) Calcula t eth em u ta tio np r o ba b ili ty according to Eq. (14); (17) Generate a random floating-point number , ∈ (0, 1);…”

Section: Simulation Results and Analysismentioning

confidence: 99%

“…For spectral clustering, its high computational complexity prevents its application to large-scale datasets. Cao et al proposed an approximate spectral clustering method to address this complexity [14]. In [15], a hybrid fuzzy Kharmonic means (HFKHM) clustering algorithm based on improved possibilistic C-means clustering and K-harmonic means was presented, which solves the noise sensitivity problem of -harmonic means and improves the memberships of the improved possibilistic -Means clustering.…”

Section: Introductionmentioning

confidence: 99%

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An Improved Clustering Method for Detection System of Public Security Events Based on Genetic Algorithm and Semisupervised Learning

et al. 2017

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The occurrence of series of events is always associated with the news report, social network, and Internet media. In this paper, a detecting system for public security events is designed, which carries out clustering operation to cluster relevant text data, in order to benefit relevant departments by evaluation and handling. Firstly, texts are mapped into three-dimensional space using the vector space model. Then, to overcome the shortcoming of the traditional clustering algorithm, an improved fuzzy -means (FCM) algorithm based on adaptive genetic algorithm and semisupervised learning is proposed. In the proposed algorithm, adaptive genetic algorithm is employed to select optimal initial clustering centers. Meanwhile, motivated by semisupervised learning, guiding effect of prior knowledge is used to accelerate iterative process. Finally, simulation experiments are conducted from two aspects of qualitative analysis and quantitative analysis, which demonstrate that the proposed algorithm performs excellently in improving clustering centers, clustering results, and consuming time.

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Section: Simulation Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Improved Clustering Method for Detection System of Public Security Events Based on Genetic Algorithm and Semisupervised Learning

et al. 2017

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“…, the spectral clustering process based on neighbor propagation is described as follows [25,26]. , σ is a scale parameter to control how fast the similarity attenuates with the distance between the ith and jth measurements [26].…”

Section: Spectral Clustering Based On Neighbor Propagationmentioning

confidence: 99%

“…, σ is a scale parameter to control how fast the similarity attenuates with the distance between the ith and jth measurements [26]. In this paper, we set σ as an empirical parameter, and let σ = 1.5. class numbers, respectively.…”

Section: Spectral Clustering Based On Neighbor Propagationmentioning

confidence: 99%

Multiple extended target tracking algorithm based on GM-PHD filter and spectral clustering

Yang

Liu

et al. 2014

EURASIP J. Adv. Signal Process.

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With the increase of the resolution of modern radars and other detection equipments, one target may produce more than one measurement. Such targets are referred to as extended targets. Recently, multiple extended target tracking (METT) has drawn a considerable attention. However, one crucial problem is how to partition the measurement sets accurately and rapidly. In this paper, an improved METT algorithm is proposed based on the Gaussian mixture probability hypothesis density (GM-PHD) filter and an effective partition method using spectral clustering technique. First, the density analysis technique is introduced to eliminate the disturbance of clutter, and then the spectral clustering technique based on neighbor propagation is used to partition the measurements. Finally, the GM-PHD filter is implemented to achieve the METT. Simulation results show that the proposed algorithm has a better performance, especially a better real-time performance, than the conventional distance partition and K-means++ methods.

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“…In order to resolve the mentioned problems, many researchers have made a lot of improvements on the related spectral clustering algorithms. Cao et al [4] proposed a approximate spectral clustering algorithm based on local information of the data points to solve the above problems. A sparse graph is firstly constructed, then the generalization performance of the algorithm is improved with the local information of the data points.…”

Section: Introductionmentioning

confidence: 99%

A Novel Spectral Clustering Based on Nonlinear Low Dimensional Embedding Feature Selection

Zhang¹,

Zhou²

2016

IJGDC

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Local information-based fast approximate spectral clustering

Cited by 25 publications

References 17 publications

An Improved Clustering Method for Detection System of Public Security Events Based on Genetic Algorithm and Semisupervised Learning

An Improved Clustering Method for Detection System of Public Security Events Based on Genetic Algorithm and Semisupervised Learning

Multiple extended target tracking algorithm based on GM-PHD filter and spectral clustering

A Novel Spectral Clustering Based on Nonlinear Low Dimensional Embedding Feature Selection

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