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Person identification is an important but still challenging problem in video surveillance. This work designs a completely automatic appearance-based person identification system, which has the ability to achieve new person discovery and classification. The proposed system consists of three modules: background and silhouette separation; feature extraction and selection; and online person identification...
The difficulties of data streams, i.e. Infinite length, the occurrence of concept-drift and the possible emergence of novel classes, are topics of high relevance in the field of recognition systems. To overcome all of these problems, the system should be updated continuously with new data while the amount of processing time should be kept small. We propose an incremental Parzen window kernel density...
Inference of network state and detection of anomaly network behavior based on the available data play important roles in the big data empowered self-organizing networks for enabling 5G. In this paper, we propose a novel framework of efficient network monitoring and proactive cell anomaly detection based on dimension reduction and fuzzy classification techniques. The enhanced semi-supervised classification...
This article introduces an original approach to understand the behavior of standard kernel spectral clustering algorithms (such as the Ng-Jordan-Weiss method) for large dimensional datasets. Precisely, using advanced methods from the field of random matrix theory and assuming Gaussian data vectors, we show that the Laplacian of the kernel matrix can asymptotically be well approximated by an analytically...
We consider the problem of adaptive Gaussian mixture learning in posterior-based distributed particle filtering, in which posteriors are approximated as Gaussian mixtures for wireless communication. We develop a hierarchical clustering algorithm to learn from weighted samples a Gaussian mixture with an adaptively determined number of components. Different from existing work, the proposed algorithm...
Data clustering is an unsupervised learning task that has found many applications in various scientific fields. The goal is to find subgroups of closely related data samples (clusters) in a set of unlabeled data. A classic clustering algorithm is the so-called k-Means. It is very popular, however, it is also unable to handle cases in which the clusters are not linearly separable. Kernel k-Means is...
Information flow detection is dedicated to tracking the dynamics and evolution of Web information spreading across the entire web over time. How to choose a comfortable information granularity to detect and how to track information evolution from one to another are the main challenges. Besides, the technological problem of doing that with a large scale information efficiently is yet to be solved....
The availability of chemical libraries with millions of compounds makes the process of identifying lead compounds very hard. The identification of these compounds is the backbone step of drug discovery process. Hierarchical clustering algorithms are used for that purpose. One of the most popular hierarchical clustering algorithms that are used in many applications in the drug discovery process is...
It has been demonstrated that the multi-channel SEMG allows assessment of anatomical and physiological individual motor unit characteristics. The motor unit action potential(MUAP) can be decomposed from SEMG to obtain these properties. This paper presented a method to exact MUAP from multi-channel SEMG. The firing instants of each motor unit(MU) were separated by K-means clustering Convolution Kernel...
in this paper, we propose a new fine-grained clustering bias field estimation and segmentation algorithm on Single Instruction Multiple Data (SIMD) architecture (GPU). The goal is to accelerate compute-intensive portions of the sequential version. We have implemented this parallel algorithm using Compute Unified Device Architecture (CUDA) on different NVidia GPU cards. The numerical results in terms...
Clustering is an important algorithm for data mining. FSC is a kind of clustering algorithm based on density, which has been proposed in the journal Science in 2014. FSC only requires one input parameter and has a higher practicability. RFSC, which is an improved algorithm of FSC algorithm, is less sensitive to the input parameters and faster. However, neither RFSC nor FSC can deal with uneven density...
In this article, Fast Global K-Means (FGKM) for Synthetic Aperture Radar (SAR) image change detection is presented. On account of the time-consuming of FGKM algorithm and the real-time demand, we present a Parallel Fast Global K-Means (P-FGKM) algorithm. We parallelize the selection of initial cluster centers which is the most time-consuming step of FGKM algorithm. The proposed algorithm is implemented...
Clustering is a task of finding natural groups in datasets based on measured or perceived similarity between data points. Spectral clustering is a well-known graph-theoretic approach, which is capable of capturing non-convex geometries of datasets. However, it generally becomes infeasible for analyzing large datasets due to relatively high time and space complexity. In this paper, we propose Multi-level...
Support vector machines (SVMs) are a widely-used machine learning technique, but they suffer from a significant drawback of high time and memory training complexity, which should be endured especially in big data problems. SVMs incorporate kernel functions — it involves selecting the kernel and induces an additional computational effort. In this paper, we address these issues and propose an SVM framework...
Stream clustering methods, which group continuous, temporally ordered dynamic data instances, have been used in a number of applications such as stock market analysis, network analysis, and cosmological analysis. Most of the popular stream clustering algorithms are linear in nature, i.e. they assume that the data is linearly separable in the input space and use measures such as the Euclidean distance...
It is important to obtain the motor unit information from multi-channel surface electromyogram (SEMG). A new decomposition method called waveform clustering convolution kernel compensation is proposed in this paper, which is classified based on waveform to improve performance. According to the number of clusters, the classification of SEMG waveform is described using a minimum distance classifier...
Flow cytometry is a technology by which the expression of multiple cellular markers are measured simultaneously for each cell. Analysis of the extracted cytometry dataset is invaluable for biologists in many applications such as identification of various cell types with specific phenotypic properties. Specifically, identification of rare subpopulation in presence of infectious diseases can reveal...
A regionalization system delineates the geographical landscape into spatially contiguous, homogeneous units for landscape ecology research and applications. In this study, we investigated a quantitative approach for developing a regional-ization system using constrained clustering algorithms. Unlike conventional clustering, constrained clustering uses domain constraints to help guide the clustering...
Mobility data records the change of location and time about the crowd activities, reflecting a large amount of semantic knowledge about human mobility and hot regions. From the perspective of regional semantic knowledge, mining anomalous regions of overcrowded area is essential for disaster-aware resilience system scheme. This paper studies how to discover anomalous regions of moving crowds over the...
Facial pose grouping plays an important role in the video face recognition. In this paper, we present an unsupervised facial pose grouping approach via Garbor subspace affinity and self-tuning spectral clustering. First, we utilize the local normalization method to reduce the impact of uneven illuminations, and then extract the discriminative appearance features via Gabor wavelet representation. Next,...
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