Constructing affinity matrix in spectral clustering based on neighbor propagation

Li, Xinye; Guo, Lijun

doi:10.1016/j.neucom.2012.06.023

Cited by 38 publications

(18 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this work, we have adopted NJW, which uses the normalized Laplacian matrix to extract the structure of the data as the standard spectral clustering (SC) for our automated clustering method. In the past recent years, different variations of SC have been proposed that showed improvement over NJW from specific perspectives including improved eigenvector selection [39,40], alternate affinity matrix generation [41], and reduced computational cost [42,43]. Nonetheless, we have adopted NJW as a seminal well-established algorithm.…”

Section: Iterative Eigengap Search With Local Scalementioning

confidence: 99%

An automated spectral clustering for multi-scale data

Afzalan

Jazizadeh

2019

Neurocomputing

View full text Add to dashboard Cite

Spectral clustering algorithms typically require a priori selection of input parameters such as the number of clusters, a scaling parameter for the affinity measure, or ranges of these values for parameter tuning. Despite efforts for automating the process of spectral clustering, the task of grouping data in multi-scale and higher dimensional spaces is yet to be explored. This study presents a spectral clustering heuristic algorithm that obviates the need for an input by estimating the parameters from the data itself. Specifically, it introduces the heuristic of iterative eigengap search with (1) global scaling and (2) local scaling. These approaches estimate the scaling parameter and implement iterative eigengap quantification along a search tree to reveal dissimilarities at different scales of a feature space and identify clusters. The performance of these approaches has been tested on various real-world datasets of power variation with multi-scale nature and gene expression. Our findings show that iterative eigengap search with a PCA-based global scaling scheme can discover different patterns with an accuracy of higher than 90% in most cases without asking for a priori input information.

show abstract

Section: Iterative Eigengap Search With Local Scalementioning

confidence: 99%

An automated spectral clustering for multi-scale data

Afzalan

Jazizadeh

2019

Neurocomputing

View full text Add to dashboard Cite

show abstract

“…The scaling parameter of step 1, σ 2 , controls how rapidly the affinity A ij falls within the distance between x i and x j [13]. D = diag(d 1 , .…”

Section: Kernel Embedding Processmentioning

confidence: 99%

Graph constrained label propagation on water supply networks

et al. 2015

View full text Add to dashboard Cite

In many real-world applications we have at our disposal a limited number of inputs in a theoretical database with full information, and another part of experimental data with incomplete knowledge for some of their features. These are cases that can be addressed by a label propagation process. It is a widely studied approach that may acquire complexity if new constraints in the new unlabeled data that should be taken into account are found. This is the case of the membership to a group or community in graphs. The proposal is to add the Laplacian matrix as well as another different similarity measures (may be not found in the original database) in the label propagation. A kernel embedding process together with a simple label propagation algorithm will be the main tools to achieve this approach by the use of all types of available information. In order to test the functionality of this new proposal, this work introduces an experimental study of biofilm development in drinking water pipes. Then, a label propagation through pipes belonging to a complete water supply network is approached. These pipes have their own properties depending on their network location and environmental co-variables. As a result, the proposal is a suitable and efficient way to deal with practical data, based on previous theoretical studies by the constrained label propagation process introduced.

show abstract

“…In recent years, spectral clustering has become one of the popular clustering approaches due to their high performance in data clustering and simplicity in implementation [25]. Compared with traditional clustering techniques, there is no need to suppose the data distribution as spheral in spectral clustering method; thus, the nonspheral distributed clusters can be recognized by using the eigenvectors of the normalized similarity matrix.…”

Section: Spectral Clustering Based On Neighbor Propagationmentioning

confidence: 99%

“…, 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%

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

Yang

Liu

et al. 2014

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

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.

show abstract

Constructing affinity matrix in spectral clustering based on neighbor propagation

Cited by 38 publications

References 3 publications

An automated spectral clustering for multi-scale data

An automated spectral clustering for multi-scale data

Graph constrained label propagation on water supply networks

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

Contact Info

Product

Resources

About