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Spectral clustering makes use of spectral-graph structure of an affinity matrix to partition data into disjoint meaningful groups. Because of its elegance, efficiency and good performance, spectral clustering has become one of the most popular clustering methods. Traditional spectral clustering assumes a single affinity matrix. However, in many applications, there could be multiple potentially useful...
A process of generating a digital hologram requires a lot of time-consuming computations. Therefore, it is important to reduce the computation time or the number of computations for achieving real-time digital holographic video generation. In this paper, we propose a method of parallelizing the computations using multiple GPUs with CUDA and OpenMP and an optimization method for reducing the computation...
In this paper, we propose an implementation of a parallel one-dimensional fast Fourier transform (FFT) on the K computer. The proposed algorithm is based on the six-step FFT algorithm, which can be altered into the recursive six-step FFT algorithm to reduce the number of cache misses. The recursive six-step FFT algorithm improves performance by utilizing the cache memory effectively. We use the recursive...
This paper presents cluster validity for kernel fuzzy clustering. First, we describe existing cluster validity indices that can be directly applied to partitions obtained by kernel fuzzy clustering algorithms. Second, we show how validity indices that take dissimilarity (or relational) data D as input can be applied to kernel fuzzy clustering. Third, we present four propositions that allow other existing...
Recent work on background subtraction has shown developments on two major fronts. In one, there has been increasing sophistication of probabilistic models, from mixtures of Gaussians at each pixel [7], to kernel density estimates at each pixel [1], and more recently to joint domainrange density estimates that incorporate spatial information [6]. Another line of work has shown the benefits of increasingly...
This paper provides a method to compute all types of singularities of non-redundant manipulators with non-helical lower pairs and designated instantaneous input and output speeds. A system of equations describing each singularity type is given. Using a numerical method based on linear relaxations, the configurations in each type are computed independently. The method is general and complete: it can...
Non-negative factorization (NMF) has been a popular machine learning method for analyzing microarray data. Kernel approaches can capture more non-linear discriminative features than linear ones. In this paper, we propose a novel kernel NMF (KNMF) approach for feature extraction and classification of microarray data. Our approach is also generalized to kernel high-order NMF (HONMF). Extensive experiments...
In this paper we present a dictionary-based framework for the reconstruction of a field of ensemble average propagators (EAPs), given a high angular resolution diffusion MRI data set. Existing techniques often consider voxel-wise reconstruction of the EAP field thereby leading to a noisy reconstruction across the field. We present a dictionary learning framework for achieving a smooth EAP reconstruction...
Unprecedented production of short reads from the new high-throughput sequencers has posed challenges to align short reads to reference genomes with high sensitivity and high speed. Many CPU-based short read aligners have been developed to address this challenge. Among them, one popular approach is the seed-and-extend heuristic. For this heuristic, the first and foremost step is to generate seeds between...
This paper presents an algorithmic approach for improving the performance of many types of stochastic dynamical simulations. The approach is to redesign existing algorithms that use sparse matrix-vector products (SPMV) with single vectors to instead use a more efficient kernel, the generalized SPMV (GSPMV), which computes with multiple vectors simultaneously. In this paper, we show how to redesign...
In this paper, an improved least squares support vector machines algorithm for solving remote sensing classification problems is presented. Support Vector Machines (SVM) is a potential remote sensing classification method because it is advantageous to deal with problems with high dimensions, small samples and uncertainty. The general idea of the proposed algorithm is that spectral angle mapping (SAM)...
Performance estimation of an application on any processor is becoming a essential task, specially when the processor is used for high performance computing. Our work here presents a model to estimate performance of various applications on a modern GPU. Recently, GPUs are getting popular in the area of high performance computing along with original application domain of graphics. We have chosen FERMI...
The methods based on empirical risk minimization are often applied to hydrocarbon discriminant in oil and gas exploration. But the predictive validities of these methods are not perfect with small sample data. This paper introduces a nonlinear support vector machine (SVM) based on structural risk minimization which can obtain global optimization other than local one and better generalization. The...
One may monitor the heart normal activity by analyzing the electrocardiogram. We propose in this paper to combine the principle of kernel machines, that maps data into a high dimensional feature space, with the autoregressive (AR) technique defined using the Yule-Walker equations, which predicts future samples using a combination of some previous samples. A pre-image technique is applied in order...
Support Vector Machine is applied to the modeling of a nonlinear dynamic system. Linear kernel is adopted in sample training and the parameters in the mathematical model are calculated by resultant lagrangian factors and support vectors. To diminish the parameter drift in identification, training samples are reconstructed by difference method. Correlation analysis demonstrates the validity of reconstruction...
This paper explores the use of quadratic mutual information as a similarity criterion for dense, non-rigid registration of medical images. Quadratic mutual information between two random variables has been recently proposed as Euclidean distance between the joint density and the product of the marginals. It has been shown to have a smooth sample estimator, that can be computed without having to use...
The autoregressive (AR) model is a well-known technique to analyze time series. The Yule-Walker equations provide a straightforward connection between the AR model parameters and the covariance function of the process. In this paper, we propose a nonlinear extension of the AR model using kernel machines. To this end, we explore the Yule-Walker equations in the feature space, and show that the model...
Many geophysical problems are computationally expensive owing to their iterative nature or due to the programs processing to large datasets. Such problems are challenging and have to be approached with extreme caution because a wrong parameter selection will not only lead to wrong results but will also take up a lot of time. The Compute Unified Device Architecture (CUDA) introduced by NVIDIA has enabled...
Spectral clustering (SC) has become one of the most popular clustering methods. Given an affinity matrix, SC explores its spectral-graph structure to partition data into disjoint meaningful groups. However, in many applications, there are multiple potentially useful features and thereby multiple affinity matrices. For applying spectral clustering to such cases, these affinity matrices must be aggregated...
Many interesting problems in the oil and gas industry face the challenge of responding to disturbances from afar. Typically, the disturbance occurs at the inlet of a pipeline or the bottom of an oil well, while sensing and actuation equipment is installed at the outlet, only, kilometers away from the disturbance. The present paper develops an output feedback control law for such cases, based on modelling...
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