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Community structure detection in complex networks is important since it can help better understand the network topology and how the network works. Compared with unsupervised method, semi supervised method may perform better. Previous works add and remove edges based on the prior information. But this method doesn't make full use of the prior information. It only used party of information to change...
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...
This paper applies fuzzy clustering algorithm to recognize the transformer winding's pressed state based on transformer's vibration signal. We propose a new semi-supervised fuzzy kernel clustering algorithm (SFKC) based on some modifications for the fuzzy clustering methods. The first modification is that the new algorithm uses prior knowledge to guide the clustering process. Second, it uses kernel...
Diagnosis of rice planthopper pests based on imaging technology is an efficient means to develop intelligent agriculture. Effective contour automation extraction is an important pretreatment technology at the early stage for identifying and classifying rice planthoppers. For the curtain as the background contained texture structures and resulted in a heterogeneous texture in the sensed image, which...
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...
Fingerprinting localization is to estimate a mobile terminal's location using its online received signal strength (RSS) measurement and offline RSS database originated from multiple access points (APs). Kernel-based fingerprinting localization is such a competitive algorithm. However, all training data need to be considered in its offline model learning stage. This render high risks for overfitting...
Fault diagnosis is an important procedure to ensure the equipment efficiency and stability. The diagnosis process is actually a pattern recognition process, and usually, the fault samples are lack of tags of fault types. In this case, the non-supervised learning method is more available, and kernel clustering is one of the most effective methods. In this paper, a novel electromagnetic particle swarm...
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...
Stochastic Gradient Descent (SGD) based method offers a viable solution to training large-scale dataset. However, the traditional SGD-based methods cannot get benefit from the distribution or geometry information carried in data. The reason is that these methods make use of the uniform distribution over the entire training set so as to sample the next data point for updating the model. We address...
Multiple-kernel k-means (MKKM) clustering has demonstrated good clustering performance by combining pre-specified kernels. In this paper, we argue that deep relationships within data and the complementary information among them can improve the performance of MKKM. To illustrate this idea, we propose a diversity-induced MKKM algorithm with extreme learning machine (ELM)-based feature extracting method...
Kernel k-means is seen as a non-linear extension of the k-means clustering method, with good performance in identifying non-isotropic and linearly inseparable clusters. However space and time requirement of kernel k-means is expensive with O(n2) complexity. Present applications with large in-memory computations make this method insuitable for large data sets. Recently, a simple prototype based hybrid...
In fuzzy clustering algorithm, fuzzy possibilistic C-means clustering algorithm (FPCM) is widely used. However, the method is sensitive to its parameters and the clustering accuracy and robustness is poor. In order to overcome the above problems, this paper presents an intuitionistic fuzzy possibilistic C-means clustering based on genetic algorithm (IFPCM-GA). IFPCM-GA does not only retain the advantages...
Kriging or Gaussian Process Regression has been successfully applied in many fields. One of the major bottlenecks of Kriging is the complexity in both processing time (cubic) and memory (quadratic) in the number of data points. To overcome these limitations, a variety of approximation algorithms have been proposed. One of these approximation algorithms is Optimally Weighted Cluster Kriging (OWCK)...
Herein, we explore both a new supervised and unsupervised technique for dimensionality reduction or multispectral sensor design via band group selection in hyperspectral imaging. Specifically, we investigate two algorithms, one based on the improved visual assessment of clustering tendency (iVAT) and the other based on the automatic extraction of “blocklike” structure in a dissimilarity matrix (CLODD...
Often in real-world applications such as web page categorization, automatic image annotations and protein function prediction, each instance is associated with multiple labels (categories) simultaneously. In addition, due to the labeling cost one usually deals with a large amount of unlabeled data while the fraction of labeled data points will typically be small. In this paper, we propose a multi-label...
A new image segmentation method is proposed in this paper for improving the effect of the image segmentation. First, an original image is nonlinear mapped into a higher dimension kernel space, and the data are better separated under the kernel space comparing with that under the original image space, then, the number of categories of the image is determined by analyzing the image histogram using gauss...
Waveform decomposition is an important step in full-waveform LiDAR remote sensing. Under the Gaussian Mixture Model, the conventional parametric classification algorithm of Expectation-Maximization (EM) is among the most widely applied ones to decompose the waveforms. This paper introduces nonparametric classification methods, such as K-means and mean-shift to decompose the LiDAR waveforms. The experiments...
In order to better model complex real-world data and to develop robust features that capture relevant information, we usually employ unsupervised feature learning to learn a layer of features representations from unlabeled data. However, developing domain-specific features for each task is expensive, time-consuming and requires expertise of the data. In this paper, we introduce multi-instance clustering...
It is well known that clustering is an unsupervised machine learning technique. However, most of the clustering methods need setting several parameters such as number of clusters, shape of clusters, or other user- or problem-specific parameters and thresholds. In this paper, we propose a new clustering approach which is fully autonomous, in the sense that it does not require parameters to be pre-defined...
In traditional multiple instance learning (MIL), both positive and negative bags are required to learn a prediction function. However, a high human cost is needed to know the label of each bag—positive or negative. Only positive bags contain our focus (positive instances) while negative bags consist of noise or background (negative instances). So we do not expect to spend too much to label the negative...
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