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The random Fourier Features method has been found very effective in approximating the kernel functions. Our former studies show that through a mixing mechanism of the feature space formed by random Fourier features and certain linear algorithms, the fuzzy clustering results in the approximated feature space are comparable to or even exceed the classical kernel-based algorithms. To increase the robustness...
Data explosion drives data analysis tools to update faster and faster, while clustering plays an indispensable role in knowledge discovery. Whereas, most of the clustering algorithms only effect on those linear separable data. Kernel-based clustering methods perform well on data sets with non-linear inner structure, but at the same time, the requirement of large memory and running time induce poor...
Although fuzzy c-means algorithm has shown great capability to spherical clusters, it can not perform very well on non-spherical data sets yet. To deal with this problem, kernel-based fuzzy clustering has been presented by mapping data points into a high-dimensional Hilbert space with kernel functions. However, the computational complexity of kernel matrix is always quadratic, usually makes kernel...
With the popularity of smart phones and the emergence of the mobile office mode, the traditional email forensics that works for computer has been already unable to satisfy the demands of reality, so forensic work needs to be expanded to a range of mobile devices, such as mobile phone, tablet, etc. In this paper, we will focus on examining if we can discover email-related information in the volatile...
The Ultra-Wide Band (UWB) signals recently have attracted increasing attention in the area of material identification due to their potential of providing very high data rates at relatively short ranges and their capability of being obtained nondestructively and contactless. The Support Vector Machines (SVM) offers one of the most robust and accurate classification capability among the well-known such...
In order to apply successfully the fuzzy clustering algorithms like shadowed C-means (SCM) to image segmentation problems, the spatial information related with each pixel in the image should be carefully calculated and appended to the clustering algorithms. In this paper, the non-local spatial information calculation is introduced to SCM. Because the data in the kernel space demonstrate more linearly-separable...
A new shadowed c-means clustering based image segmentation method is proposed in this paper. By including the local spatial information in shadowed c-means algorithm and mapping the original data into a high dimensional space via kernel method, we propose the Kernel Spatial Shadowed C-Means (KSSCM) clustering algorithm for image segmentation problems. The KSSCM based approach shows better performance...
Proximity-based fuzzy c-means algorithm (P-FCM), a classical semi-supervised clustering algorithm, concerns with the number of proximity “hints” or constraints that specify an extent to which some pairs of instances are considered similar or. By replacing the fuzzy c-means in P-FCM with a kernel fuzzy c-means, this paper proposes a new semi-supervised clustering algorithm named proximity-based kernel...
Using multi-GPU systems, including GPU clusters, is gaining popularity in scientific computing. However, when using multiple GPUs concurrently, the conventional data parallel GPU programming paradigms, e.g., CUDA, cannot satisfactorily address certain issues, such as load balancing, GPU resource utilization, overlapping fine grained computation with communication, etc. In this paper, we present a...
In this paper, a generalized multiple-kernel fuzzy C-means (FCM) (MKFCM) methodology is introduced as a framework for image-segmentation problems. In the framework, aside from the fact that the composite kernels are used in the kernel FCM (KFCM), a linear combination of multiple kernels is proposed and the updating rules for the linear coefficients of the composite kernel are derived as well. The...
The computational power provided by many-core graphics processing units (GPUs) has been exploited in many applications. The programming techniques supported and employed on these GPUs and Multi-GPUs systems are not sufficient to address problems exhibiting irregular, and unbalanced workload such as Molecular Dynamic (MD) simulations of systems with non-uniform densities. In this paper, we propose...
The computational power provided by many-core graphics processing units (GPUs) has been exploited in many applications. The programming techniques currently employed on these GPUs are not sufficient to address problems exhibiting irregular, and unbalanced workload. The problem is exacerbated when trying to effectively exploit multiple GPUs concurrently, which are commonly available in many modern...
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