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Multiple Kernel Fuzzy Clustering
2012
IEEE transactions on fuzzy systems
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By incorporating multiple kernels and automatically adjusting the kernel weights, MKFC is more immune to ineffective kernels and irrelevant features. This makes the choice of kernels less crucial. ...
By applying kernel tricks, the kernel fuzzy c-means algorithm attempts to address this problem by mapping data with nonlinear relationships to appropriate feature spaces. ...
in the kernel space, which allows more freedom for prototypes in the feature space. ...
doi:10.1109/tfuzz.2011.2170175
fatcat:u4qtgpm4mnfo7py3enqvzh7wfu
An Integrated Cluster Detection, Optimization, and Interpretation Approach for Financial Data
2021
IEEE Transactions on Cybernetics
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data in the clusters share greater similarities, and thus, the clusters can be easily interpreted with eigenvectors. ...
In many financial applications, such as fraud detection, reject inference, and credit evaluation, detecting clusters automatically is critical because it helps to understand the subpatterns of the data ...
(c) Distribution of features' weights in cluster 3. (d) Distribution of features' weights in cluster 4. (e) Distribution of features' weights in cluster 5. ...
doi:10.1109/tcyb.2021.3109066
pmid:34550896
fatcat:pfomad72lnf2tbpxx23a6jwbe4
Medical Image Segmentation And Detection Of Mr Images Based On Spatial Multiple-Kernel Fuzzy C-Means Algorithm
2015
Zenodo
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A linear combination of multiples kernels with spatial information is used in the kernel FCM (KFCM) and the updating rules for the linear coefficients of the composite kernels are derived as well. ...
Fuzzy cmeans (FCM) based techniques have been widely used in medical image segmentation problem due to their simplicity and fast convergence. ...
Kernel Fuzzy C-means (KFCM) Algorithm In KFCM the cluster centers in the kernel space are mapped from the original data space or the feature space [15] . ...
doi:10.5281/zenodo.1109072
fatcat:2ctc23j3zjd63egbnbzmomno74
Kernel Codebooks for Scene Categorization
[chapter]
2008
Lecture Notes in Computer Science
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Both of these drawbacks stem from the hard assignment of visual features to a single codeword. ...
This paper introduces a method for scene categorization by modeling ambiguity in the popular codebook approach. ...
Radius-based clustering ensures an even distribution of codewords over feature space and has been shown to outperform the popular k-means algorithm [12] . ...
doi:10.1007/978-3-540-88690-7_52
fatcat:mqgkgyi6lbayhfjxxhkyvnlun4
Kernel-Based Weighted Multi-view Clustering
2012
2012 IEEE 12th International Conference on Data Mining
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In this work, views are expressed in terms of given kernel matrices and a weighted combination of the kernels is learned in parallel to the partitioning. ...
Weights assigned to kernels are indicative of the quality of the corresponding views' information. ...
) and ( 8 ) it is obvious that the intra-cluster variance in feature space H is the weighted sum of the intracluster variances of the individual views' feature spaces, H (v) , under a common clustering ...
doi:10.1109/icdm.2012.43
dblp:conf/icdm/TzortzisL12
fatcat:tebybdhwxzcqjksue26eoqtc6m
COMPUTER VISION-BASED COLOR IMAGE SEGMENTATION WITH IMPROVED KERNEL CLUSTERING
2015
International Journal on Smart Sensing and Intelligent Systems
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We developed an improved kernel clustering algorithm for computing the different clusters of given color images in kernel-induced space for image segmentation. ...
However, traditional clustering methods do not scale well with the number of data, which limits the ability of handling massive data effectively. ...
ACKNOWLEDGMENTS The authors are grateful to the anonymous referees for their valuable comments and suggestions to improve the presentation of this paper. ...
doi:10.21307/ijssis-2017-826
fatcat:tr5mkotg25gnlfituu7cwgvxbi
Visual Word Ambiguity
2010
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Moreover, we show that our method profits in high-dimensional feature spaces and reaps higher benefits when increasing the number of image categories. ...
Despite the clear mismatch of this hard assignment with the nature of continuous features, the approach has been applied successfully for some years. ...
ACKNOWLEDGMENT The authors like to thank Koen van de Sande, for his work on the Pascal VOC set. This work was supported by MultimediaN. ...
doi:10.1109/tpami.2009.132
pmid:20489229
fatcat:pbxxnjarlfd7bdxvzljunz4aqy
Adaptive kernel fuzzy clustering for missing data
2021
PLoS ONE
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This work aims to propose and evaluate a Kernel Fuzzy C-means clustering algorithm considering the kernelization of the metric with local adaptive distances (VKFCM-K-LP) under three types of strategies ...
The third technique, called Optimal Completion Strategy (OCS), computes missing values iteratively as auxiliary variables in the optimization of a suitable objective function. ...
Telmo de Menezes e Silva Filho for proofreading and technical comments in the new version of this manuscript. The authors thank to CAPES and CNPq, Brazil. ...
doi:10.1371/journal.pone.0259266
pmid:34767560
pmcid:PMC8589222
fatcat:ca5xdlapm5a2jaewk4cr573qfy
Dental X-ray Image Segmentation using Gaussian Kernel-Based in Conditional Spatial Fuzzy C-means
2017
International Journal on Advanced Science, Engineering and Information Technology
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In this paper, we proposed a new conditional spatial fuzzy C-means algorithm with Gaussian kernel function to facilitate dental X-ray image segmentation. ...
The Gaussian kernel function is used as an objective function of conditional spatial fuzzy C-means algorithm to substitute the Euclidian distance. ...
ACKNOWLEDGMENT The author would like to thank Ministry of Research, Technology and Higher Education, the Republic of Indonesia which support the scholarship of the doctoral program. ...
doi:10.18517/ijaseit.7.6.3073
fatcat:3y2bwprmdffhboivjnzpvnengu
SpectralCAT: Categorical spectral clustering of numerical and nominal data
2012
Pattern Recognition
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This is achieved by automatic non-linear transformations, which identify geometric patterns in the data, and find the connections among them while projecting them onto low-dimensional spaces. ...
This is done by discovering the optimal transformation according to the Calinski-Harabasz index for each feature and attribute in the dataset. ...
It uses a kernel function, which is based on Hamming distance, to embed categorical data in a constructed feature space where the clustering takes place. ...
doi:10.1016/j.patcog.2011.07.006
fatcat:llyg66ortvgu7khdqbtd6bbv2a
Speaker specific feature based clustering and its applications in language independent forensic speaker recognition
2020
International Journal of Electrical and Computer Engineering (IJECE)
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Then a clustering approach is proposed based on the peak approximation in order to maximize the similarities between language-independent utterances within all clusters. ...
Most existing methods measure inter-utterance similarities directly based on spectrum-based characteristics, the resulting clusters may not be well related to speaker's, but rather to different acoustic ...
The key idea behind the kernel technique was originally presented in [15] and applied again in connection with the general purpose SVM [16, 17, 18] followed by other kernel-based methods. ...
doi:10.11591/ijece.v10i4.pp3508-3518
fatcat:4iarg74bcvezlaiovbzh5r5b7m
Soft clustering using weighted one-class support vector machines
2009
Pattern Recognition
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The key building block of our method is the weighted OC-SVM (WOC-SVM), a novel tool introduced in this paper, based on which an expectation-maximization-type soft clustering algorithm is defined. ...
The proposed algorithm extends a previously proposed hard clustering algorithm, also based on OC-SVM representation of clusters. ...
Acknowledgments The authors would thank Annalisa Barla for initial discussions on WOC-SVM and Cheng Dong Seon for precious help in adapting the LISBSVM code to the weighted case. ...
doi:10.1016/j.patcog.2008.07.004
fatcat:ro75p3surbeixb3xzoxyxuea5q
Gaussian Kernelized Fuzzy c-means with Spatial Information Algorithm for Image Segmentation
2012
Journal of Computers
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In this paper, Gaussian kernel-based fuzzy c-means algorithm with spatial information (KSFCM) is proposed. KSFCM is more robust and adaptive. ...
These methods extend FCM from two aspects, one is replacing the Euclidean norm, and the other is considering the spatial information constraints for clustering. ...
ACKNOWLEDGMENT The authors would like to thank the anonymous reviewers for their detailed review and constructive comments. ...
doi:10.4304/jcp.7.6.1511-1518
fatcat:e5f7ycyfsjdzjb3khtxelxpcja
Prediction Algorithms: A Study
2018
Asian Journal of Computer Science and Technology
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Prediction algorithms make a prognosis of the future in a scientific way by analysing the data. They are being applied successfully to the problems in various fields and find good solutions. ...
The paper summarizes the advantages & disadvantages of the prediction algorithms and the challenges to be addressed in the prediction field. ...
It makes predictions based on a linear predictor function combining a set of weights with the feature vector. ...
doi:10.51983/ajcst-2018.7.3.1896
fatcat:bteiqgxw2beabb4oe6lmasuyv4
Automated Detection Of Hard Exudates In Fundus Images Using Improved Otsu Thresholding And Svm
2018
Zenodo
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Our database was composed of 120 images with variable color, brightness, and quality. 70 of them were used to train the SVM and the remaining 50 to assess the performance of the method. ...
Automatic recognition of hard exudates (EXs) which is one of DR lesions in fundus images can contribute to the diagnosis and screening of DR.The aim of this paper was to automatically detect those lesions ...
ACKNOWLEDGEMENTS We would like to thank Department of Ophthalmology, Jiangsu province hospital of TCM who supplied all the images used in this project for its great support for the project. ...
doi:10.5281/zenodo.1252213
fatcat:7uqzs3gifzdrpnn6xivupdwvly
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