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Fusion Multiple Kernel K-means
Proceedings of the AAAI Conference on Artificial Intelligence, 2022Multiple kernel clustering aims to seek an appropriate combination of base kernels to mine inherent non-linear information for optimal clustering. Late fusion algorithms generate base partitions independently and integrate them in the following clustering procedure, improving the overall efficiency. However, the separate base partition generation leads
Yi Zhang 0104 +6 more
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Adaptive Explicit Kernel Minkowski Weighted K-means
Information Sciences, 2022 The K-means algorithm is among the most commonly used data clustering methods. However, the regular K-means can only be applied in the input space and it is applicable when clusters are linearly separable. The kernel K-means, which extends K-means into the kernel space, is able to capture nonlinear structures and identify arbitrarily shaped clusters ...
Maryam Amir Haeri +1 more
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Kernel K-Means Sampling for Nyström Approximation
IEEE Transactions on Image Processing, 2018A fundamental problem in Nyström-based kernel matrix approximation is the sampling method by which training set is built. In this paper, we suggest to use kernel -means sampling, which is shown in our works to minimize the upper bound of a matrix approximation error.
Li He
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Supervised kernel density estimation K-means
Expert Systems With Applications, 2021Abstract K-means is a well-known unsupervised-learning algorithm. It assigns data points to k clusters, the centers of which are termed centroids. However, these centroids have a structure usually represented by a list of quantized vectors, so that kernel density estimation models can better represent complex data distributions.
Patrick Marques Ciarelli
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Multiple Kernel k-Means with Incomplete Kernels
Proceedings of the AAAI Conference on Artificial Intelligence, 2017Multiple kernel clustering (MKC) algorithms optimally combine a group of pre-specified base kernels to improve clustering performance. However, existing MKC algorithms cannot efficiently address the situation where some rows and columns of base kernels are absent.
Xinwang Liu 0002 +5 more
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Secrets of GrabCut and Kernel K-Means
2015 IEEE International Conference on Computer Vision (ICCV), 2015The log-likelihood energy term in popular model-fitting segmentation methods, e.g. [39, 8, 28, 10], is presented as a generalized "probabilistic K-means" energy [16] for color space clustering. This interpretation reveals some limitations, e.g. over-fitting.
Meng Tang 0001 +3 more
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2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07, 2007
In this paper we present a kernel method for data clustering, where the soft k-means is carried out in a feature space, instead of input data space, leading to soft kernel k-means. We also incorporate a geodesic kernel into the soft kernel k-means, in order to take the data manifold structure into account.
Jaehwan Kim +2 more
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In this paper we present a kernel method for data clustering, where the soft k-means is carried out in a feature space, instead of input data space, leading to soft kernel k-means. We also incorporate a geodesic kernel into the soft kernel k-means, in order to take the data manifold structure into account.
Jaehwan Kim +2 more
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Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, 2004
Kernel k-means and spectral clustering have both been used to identify clusters that are non-linearly separable in input space. Despite significant research, these methods have remained only loosely related. In this paper, we give an explicit theoretical connection between them.
Inderjit S. Dhillon +2 more
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Kernel k-means and spectral clustering have both been used to identify clusters that are non-linearly separable in input space. Despite significant research, these methods have remained only loosely related. In this paper, we give an explicit theoretical connection between them.
Inderjit S. Dhillon +2 more
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Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, 2011
Digital data explosion mandates the development of scalable tools to organize the data in a meaningful and easily accessible form. Clustering is a commonly used tool for data organization. However, many clustering algorithms designed to handle large data sets assume linear separability of data and hence do not perform well on real world data sets ...
Radha Chitta +3 more
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Digital data explosion mandates the development of scalable tools to organize the data in a meaningful and easily accessible form. Clustering is a commonly used tool for data organization. However, many clustering algorithms designed to handle large data sets assume linear separability of data and hence do not perform well on real world data sets ...
Radha Chitta +3 more
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On Clustering Scheme for Kernel K-Means
Journal of Al-Rafidain University College For Sciences ( Print ISSN: 1681-6870 ,Online ISSN: 2790-2293 ), 2021Cluster analysis mainly concerned with dividing the number of data elements into clusters observation in the same cluster are homogeneous and are not homogeneous with other clusters, but in the case of nonparametric data it is not possible to deal with classic estimated because of obtaining misleading results This gave rise to adopt efficient ...
Lekaa Muhamed, Hayder Mohammed
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