Powered Gaussian kernel spectral clustering

Nataliani, Yessica; Yang, Miin-Shen

doi:10.1007/s00521-017-3036-2

Cited by 37 publications

(16 citation statements)

References 28 publications

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“…The proposed techniques are compared with the following methods: spectral clustering algorithm (NJW) by Ng et al [19] (2002), Neighbour Propagation (NP) (2012) proposed by Li and Guo [16], Shared Nearest Neighbours (SNN) (2016) proposed by Ye and Sakurai. [31], Powered Gaussian (PG) (2017) by Nataliani et al [18], Spectral clustering using Local PCA (LPCA) [1] (2017), Powered Ratio Cut (PRCUT) [4] (2018). In the experiments conducted, three metrics were used for comparison: Adjusted Rand Index (ARI) [20], Normalized Mutual Information (NMI) [24] and Clustering Error (CE) [11].…”

Section: Results and Analysismentioning

confidence: 99%

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Enhanced Affinity for Spectral Clustering using Topological Node Features (TNFS)

Chintalapati,

Rachakonda

2019

IJEAT

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Data clustering is an active topic of research as it has applications in various fields such as biology, management, statistics, pattern recognition, etc. Spectral Clustering (SC) has gained popularity in recent times due to its ability to handle complex data and ease of implementation. A crucial step in spectral clustering is the construction of the affinity matrix, which is based on a pairwise similarity measure. The varied characteristics of datasets affect the performance of a spectral clustering technique. In this paper, we have proposed an affinity measure based on Topological Node Features (TNFs) viz., Clustering Coefficient (CC) and Summation index (SI) to define the notion of density and local structure. It has been shown that these features improve the performance of SC in clustering the data. The experiments were conducted on synthetic datasets, UCI datasets, and the MNIST handwritten datasets. The results show that the proposed affinity metric outperforms several recent spectral clustering methods in terms of accuracy.

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Section: Results and Analysismentioning

confidence: 99%

“…Inspired by this formulation of similarity, powered spectral clustering has been proposed by Nataliani et al [18]. In this method, the similarity measure is given as:…”

Section: Other Recent Methodsmentioning

confidence: 99%

Enhanced Affinity for Spectral Clustering using Topological Node Features (TNFS)

Chintalapati,

Rachakonda

2019

IJEAT

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“…e experiment environment is Matlab 2016b. In the experiment, we compare the proposed algorithm with the K-means, NJW [14], MPSC algorithm [22], PGSC algorithm [17], and SC-NP algorithm [23] on four artificial data sets and seven UCI data sets. e proposed algorithm will also use the image in the BSDS500 data set for image segmentation.…”

Section: Computational Complexitymentioning

confidence: 99%

“…To solve this problem, Zhang et al [16] proposed a construction method of the similarity matrix based on local density. Nataliani and Yang [17] proposed an energy Gaussian kernel function to solve this problem.…”

Section: Introductionmentioning

confidence: 99%

Spectral Clustering Algorithm Based on Improved Gaussian Kernel Function and Beetle Antennae Search with Damping Factor

Zhang

Liu

Wang

2020

Computational Intelligence and Neuroscience

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There are two problems in the traditional spectral clustering algorithm. Firstly, when it uses Gaussian kernel function to construct the similarity matrix, different scale parameters in Gaussian kernel function will lead to different results of the algorithm. Secondly, K-means algorithm is often used in the clustering stage of the spectral clustering algorithm. It needs to initialize the cluster center randomly, which will result in the instability of the results. In this paper, an improved spectral clustering algorithm is proposed to solve these two problems. In constructing a similarity matrix, we proposed an improved Gaussian kernel function, which is based on the distance information of some nearest neighbors and can adaptively select scale parameters. In the clustering stage, beetle antennae search algorithm with damping factor is proposed to complete the clustering to overcome the problem of instability of the clustering results. In the experiment, we use four artificial data sets and seven UCI data sets to verify the performance of our algorithm. In addition, four images in BSDS500 image data sets are segmented in this paper, and the results show that our algorithm is better than other comparison algorithms in image segmentation.

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“…The critical step in SC is the construction of the affinity matrix, which encapsulates the similarity between the data points. It has been shown in the literature [3,4,5] that local features such as color, density, and texture play an important role in enhancing the pairwise affinity. SC has been used to provide a solution to the image segmentation problem in the works proposed by Shi et al [1], Chang and Yeung [6], and Fowlkes et al [2].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Similarity for Spectral Clustering using Local Steering Features

Chintalapati*¹,

Rachakonda²

2019

IJITEE

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In the field of clustering, spectral clustering (SC) has become an effective tool to analyze complex non-convex data using only pairwise affinity between the data points. Many novel affinity metrics have been proposed in the literature which use local features such as color, spatial coordinates, and texture. Some of these methods used SC for image segmentation [1, 2]. In this work, we have used the covariance matrix of the pixels in a patch and proposed an orientation based feature of a pixel called steering feature. This feature is robust and data-driven. The steering feature is used to enhance the construction of affinity metric for spectral clustering proposed by Shi and Malik [1]. Using the Nystrom framework [2] on images from BSD500 benchmark dataset, we have shown that the proposed affinity metric gives better result than Shi and Malik [1]

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Powered Gaussian kernel spectral clustering

Cited by 37 publications

References 28 publications

Enhanced Affinity for Spectral Clustering using Topological Node Features (TNFS)

Enhanced Affinity for Spectral Clustering using Topological Node Features (TNFS)

Spectral Clustering Algorithm Based on Improved Gaussian Kernel Function and Beetle Antennae Search with Damping Factor

Enhanced Similarity for Spectral Clustering using Local Steering Features

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