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A novel version of multi-class classification method based on fruit fly optimization algorithm (FOA) and relevance vector machine (RVM) is proposed. The one-against-one-against-rest (OAOAR) classification model based on the traditional one-against-one (OAO) and one-against-rest (OAR) algorithm is aimed at combining the advantages of them and translates the multi-class classification problem into multiple...
We address the problem of how to design a more effective co-training scheme to tackle the multi-view spectral clustering. The conventional co-training procedure treats information from all views equally and often converges to a compromised consensus view that does not fully utilize the multiview information. We instead propose to learn an augmented view and construct its corresponding affinity matrix...
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This paper proposes a novel method for designing compactly supported biorthogonal graph wavelet filter banks with flat spectral responses. We firstly construct a class of biorthogonal graph filter banks by using the polynomial half-band kernels, and then present a design method for the polynomial half-band kernel. The proposed design method utilizes the PBP (Parametric Bernstein Polynomial), which...
This paper puts forward a new tracking algorithm based on Mean Shift algorithm and the Particle Filter algorithm. We combined the two algorithms efficiently based on the open structure system of both Mean Shift algorithm and the Particle Filter algorithm, and the model similar expression of establishment of the target, similarity measure and the selection of kernel function they have. The new algorithm...
As the K-means algorithm is dependent on the initial clustering center, and the particle swarm optimization (PSO) converges prematurely and is easily trapped in local minima, a Gaussian kernel particle swarm optimization clustering algorithm is proposed in this paper. The algorithm adopts the theory of good point set to initialize population, which makes the initial clustering center more rational...
In this study, we propose three new algorithms based on difference of convex (DC) programming and DC algorithm (DCA) for kernel fuzzy c-means (KFCM) clustering model. Firstly, KFCM model is reformulated into two equivalent forms of DC programmings for which different KFCM algorithms are designed. Then, to further accelerate the second DCA based KFCM algorithm, we adopt an approximate strategy which...
Despite its popularity, deploying Convolutional Neural Networks (CNNs) on a portable system is still challenging due to large data volume, intensive computation and frequent memory access. Although previous FPGA acceleration schemes generated by high-level synthesis tools (i.e., HLS, OpenCL) have allowed for fast design optimization, hardware inefficiency still exists when allocating FPGA resources...
Predictive maintenance task is of crucial role for any plant equipment supervision and scheduling of service activities. For this purpose it should be known what is current aging status of any equipment. Presented approach assumes that we know the nominal (starting) element curve and a damage one as well. It is also assumed that the aging course progresses according to some good practice aging Lorentz...
FPGAs are promising platforms to efficiently execute distributed graph algorithms. Unfortunately, they are notoriously hard to program, especially when the problem size and system complexity increases. In this paper, we propose GraVF, a high-level design framework for distributed graph processing on FPGAs. It leverages the vertex-centric paradigm, which is naturally distributed and requires the user...
A new algorithm for apple disease image segmentation is proposed. A fuzzy factor for weighted balance is introduced in the algorithm to describe the coefficient of spatial constraints between pixels in neighborhood. For enhancing the integrality of neighbor information, the space distance constraints and the spatial gray constraints are considered. The fuzzy factor in the neighborhood is used to keep...
In order to improve prediction accuracy of power load and guarantee safe power supply, this paper proposes a new power load prediction method based on particle swarm optimization optimizing and supporting vector machine(PSO-SVM). It is also applied in data analysis sub-system of power dispatching automation system, designs and completes a set of periodic data set and periodic association rule mining-based...
With the development of hyperspectral remote sensing information processing, hyperspectral image classification becomes a hot topic. The algorithm of kernel sparse representation classification based on spatial-spectral graph regularization and sparsity concentration index (SSGSCI-KSRC) gains a good result. Due to the big scale of hyperspectral image data, time-critical requirement in the practical...
As the symbol of the partition clustering method, K-Means is well known and widely used in many fields for the easily implemented and high efficiency. However, the initial center problem may affect the final cluster result, sometimes the final cluster result might contain some empty clusters. In this paper, a new K-Mean initialization method is proposed which combines the statistical information and...
Within the supervised machine learning framework, classifier performance is significantly affected by the size of training datasets. One of the ways to improve classification accuracy with small training datasets is to utilize additional knowledge about training data that is not present in testing data. In the Learning Using Privileged Information (LUPI) learning paradigm, this additional knowledge...
As new security intrusions arise so does the demand for viable intrusion detection systems These solutions must deal with huge data volumes, high speed network traffics and countervail new and various types of security threats. In this paper we combine existing technologies to construct an Anomaly based Intrusion Detection System. Our approach improves the Support Vector Machine classifier by exploiting...
Recently, deep neural networks have been widely used in many applications with successful results. Each layer of multilayer neural networks can be viewed as non-linear dimension expansion and the final layer can be viewed as linear classification. It is observed that adding more layers doesn't always improve the classification performance in some cases. In this paper, we analyze the discriminant powers...
Feature selection is an effective technique for dimensionality reduction to get the most useful information from huge raw data. Many spectral feature selection algorithms have been proposed to address the unsupervised feature selection problem, but most of them fail to pay attention to the noises induced during the feature selection process. In this paper, we not only consider the feature structural...
Kernel independent component analysis (KICA) has an important application in blind source separation, in which how to select the optimal kernel, including the kernel functional form and its parameters, is the key issue for obtaining the optimal performance. In practices, a single kernel is usually chosen as the kernel model of KICA in light of experience. However, selecting a suitable kernel model...
In order to reduce the computational complexity of kernel machines and improve their performance in multi-label classification, we develop a systematic two step batch approach for constructing and training a new multiclass kernel machine (MKM). The proposed paradigm prunes the kernels, and uses Newton's method to improve the kernel parameters. In each iteration, output weights are found using orthogonal...
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