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  • 2016  (24)
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  • 2016  (24)
  • 1
    Description: Outliers are samples that are generated by different mechanisms from other normal data samples. Graphs, in particular social network graphs, may contain nodes and edges that are made by scammers, malicious programs or mistakenly by normal users. Detecting outlier nodes and edges is important for data mining and graph analytics. However, previous research in the field has merely focused on detecting outlier nodes. In this article, we study the properties of edges and propose outlier edge detection algorithms using two random graph generation models. We found that the edge-ego-network, which can be defined as the induced graph that contains two end nodes of an edge, their neighboring nodes and the edges that link these nodes, contains critical information to detect outlier edges. We evaluated the proposed algorithms by injecting outlier edges into some real-world graph data. Experiment results show that the proposed algorithms can effectively detect outlier edges. In particular, the algorithm based on the Preferential Attachment Random Graph Generation model consistently gives good performance regardless of the test graph data. Further more, the proposed algorithms are not limited in the area of outlier edge detection. We demonstrate three different applications that benefit from the proposed algorithms: 1) a preprocessing tool that improves the performance of graph clustering algorithms; 2) an outlier node detection algorithm; and 3) a novel noisy data clustering algorithm. These applications show the great potential of the proposed outlier edge detection techniques. Comment: 14 pages, 5 figures, journal paper
    Keywords: Computer Science - Social And Information Networks ; Physics - Physics And Society
    Source: Cornell University
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  • 2
    Language: English
    In: Neural Computing and Applications, 7/29/2016
    Description: To access, purchase, authenticate, or subscribe to the full-text of this article, please visit this link: http://dx.doi.org/10.1007/s00521-016-2504-4 Byline: Jenni Raitoharju (1), Serkan Kiranyaz (2), Moncef Gabbouj (1) Keywords: Content-based image retrieval and classification; Feature synthesis; Multi-dimensional particle swarm optimization; Multi-layer perceptrons Abstract: Most existing content-based image retrieval and classification systems rely on low-level features which are automatically extracted from images. However, often these features lack the discrimination power needed for accurate description of the image content, and hence, they may lead to a poor retrieval or classification performance. We propose a novel technique to improve low-level features which uses parallel one-against-all perceptrons to synthesize new features with a higher discrimination power which in turn leads to improved classification and retrieval results. The proposed method can be applied on any database and low-level features as long as some ground-truth information is available. The main merits of the proposed technique are its simplicity and faster computation compared to existing feature synthesis methods. Extensive simulation results show a significant improvement in the features' discrimination power. Author Affiliation: (1) 0000 0000 9327 9856, grid.6986.1, Department of Signal Processing, Tampere University of Technology, PO Box 527, 33101, Tampere, Finland (2) 0000 0004 0634 1084, grid.412603.2, Electrical Engineering Department, College of Engineering, Qatar University, Doha, Qatar Article History: Registration Date: 20/07/2016 Received Date: 01/04/2014 Accepted Date: 20/07/2016 Online Date: 29/07/2016
    Keywords: Electrical Engineering ; Signal Processing ; Optimization Theory;
    ISSN: 0941-0643
    E-ISSN: 1433-3058
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  • 3
    Language: English
    In: IEEE Transactions on Knowledge and Data Engineering, 01 November 2016, Vol.28(11), pp.2884-2894
    Description: In this study, we propose a novel vector quantization algorithm for Approximate Nearest Neighbor (ANN) search, based on a joint competitive learning strategy and hence called as competitive quantization (CompQ). CompQ is a hierarchical algorithm, which iteratively minimizes the quantization error by jointly optimizing the codebooks in each layer, using a gradient decent approach. An extensive set of experimental results and comparative evaluations show that CompQ outperforms the-state-of-the-art while retaining a comparable computational complexity.
    Keywords: Vector Quantization ; Encoding ; Optimization ; Hamming Distance ; Electronic Mail ; Nearest Neighbor Searches ; Approximate Nearest Neighbor Search ; Binary Codes ; Large-Scale Retrieval ; Vector Quantization ; Engineering ; Computer Science
    ISSN: 1041-4347
    E-ISSN: 1558-2191
    Source: IEEE Conference Publications
    Source: IEEE Journals & Magazines 
    Source: IEEE Xplore
    Source: IEEE Journals & Magazines
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  • 4
    Language: English
    In: Journal of Big Data, 2016, Vol.3(1), pp.1-22
    Description: Graph clustering is an important technique to understand the relationships between the vertices in a big graph. In this paper, we propose a novel random-walk-based graph clustering method. The proposed method restricts the reach of the walking agent using an inflation function and a normalization function. We analyze the behavior of the limited random walk procedure and propose a novel algorithm for both global and local graph clustering problems. Previous random-walk-based algorithms depend on the chosen fitness function to find the clusters around a seed vertex. The proposed algorithm tackles the problem in an entirely different manner. We use the limited random walk procedure to find attractor vertices in a graph and use them as features to cluster the vertices. According to the experimental results on the simulated graph data and the real-world big graph data, the proposed method is superior to the state-of-the-art methods in solving graph clustering problems. Since the proposed method uses the embarrassingly parallel paradigm, it can be efficiently implemented and embedded in any parallel computing environment such as a MapReduce framework. Given enough computing resources, we are capable of clustering graphs with millions of vertices and hundreds millions of edges in a reasonable time.
    Keywords: Graph clustering ; Random walk ; Big data ; Community finding
    E-ISSN: 2196-1115
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  • 5
    Language: English
    In: Pattern Recognition Letters, 01 March 2016, Vol.72, pp.91-99
    Description: In this paper, a learn-to-rank algorithm is proposed and applied over the segment pool of salient objects generated by an extension of the unsupervised Quantum-Cuts algorithm. Quantum Cuts is extended in a multiresolution approach as follows. First, superpixels are extracted from the input image using the simple linear iterative k-means algorithm; second, a scale space decomposition is applied prior to Quantum Cuts in order to capture salient details at different scales; and third, multispectral approach is followed to generate multiple proposals instead of a single proposal as in Quantum Cuts. The proposed learn-to-rank algorithm is then applied to these multiple proposals in order to select the most appropriate one. Shape and appearance features are extracted from the proposed segments and regressed with respect to a given confidence measure resulting in a ranked list of proposals. This ranking yields consistent improvements in an extensive collection of benchmark datasets containing around 18k images. Our analysis on the random forest regression models that are trained on different datasets shows that, although these datasets are of quite different characteristics, a model trained in the most complex dataset consistently provides performance improvements in all the other datasets, hence yielding robust salient object segmentation with a significant performance gap compared to the competing methods.
    Keywords: Quantum Cuts ; Saliency Detection ; Learning to Rank ; Salient Object Segmentation ; Multispectral Analysis ; Engineering ; Computer Science
    ISSN: 0167-8655
    E-ISSN: 1872-7344
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  • 6
    Language: English
    In: IEEE Transactions on Biomedical Engineering, March 2016, Vol.63(3), pp.664-675
    Description: Goal: This paper presents a fast and accurate patient-specific electrocardiogram (ECG) classification and monitoring system. Methods: An adaptive implementation of 1-D convolutional neural networks (CNNs) is inherently used to fuse the two major blocks of the ECG classification into a single learning body: feature extraction and classification. Therefore, for each patient, an individual and simple CNN will be trained by using relatively small common and patient-specific training data, and thus, such patient-specific feature extraction ability can further improve the classification performance. Since this also negates the necessity to extract hand-crafted manual features, once a dedicated CNN is trained for a particular patient, it can solely be used to classify possibly long ECG data stream in a fast and accurate manner or alternatively, such a solution can conveniently be used for real-time ECG monitoring and early alert system on a light-weight wearable device. Results: The results over the MIT-BIH arrhythmia benchmark database demonstrate that the proposed solution achieves a superior classification performance than most of the state-of-the-art methods for the detection of ventricular ectopic beats and supraventricular ectopic beats. Conclusion: Besides the speed and computational efficiency achieved, once a dedicated CNN is trained for an individual patient, it can solely be used to classify his/her long ECG records such as Holter registers in a fast and accurate manner. Significance: Due to its simple and parameter invariant nature, the proposed system is highly generic, and, thus, applicable to any ECG dataset.
    Keywords: Electrocardiography ; Neurons ; Feature Extraction ; Kernel ; Databases ; Training ; Monitoring ; Patient-Specific ECG Classification ; Convolutional Neural Networks ; Real-Time Heart Monitoring ; Medicine ; Engineering
    ISSN: 0018-9294
    E-ISSN: 1558-2531
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  • 7
    Language: English
    In: IEEE Transactions on Knowledge and Data Engineering, 01 July 2016, Vol.28(7), pp.1722-1733
    Description: Approximate Nearest Neighbor (ANN) search has become a popular approach for performing fast and efficient retrieval on very large-scale datasets in recent years, as the size and dimension of data grow continuously. In this paper, we propose a novel vector quantization method for ANN search which enables faster and more accurate retrieval on publicly available datasets. We define vector quantization as a multiple affine subspace learning problem and explore the quantization centroids on multiple affine subspaces. We propose an iterative approach to minimize the quantization error in order to create a novel quantization scheme, which outperforms the state-of-the-art algorithms. The computational cost of our method is also comparable to that of the competing methods.
    Keywords: Principal Component Analysis ; Artificial Neural Networks ; Vector Quantization ; Iterative Methods ; Encoding ; Nearest Neighbor Searches ; Approximate Nearest Neighbor Search ; Binary Codes ; Large-Scale Retrieval ; Subspace Clustering ; Vector Quantization ; Engineering ; Computer Science
    ISSN: 1041-4347
    E-ISSN: 1558-2191
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  • 8
    Language: English
    In: IEEE Transactions on Neural Networks and Learning Systems, December 2016, Vol.27(12), pp.2458-2471
    Description: In training radial basis function neural networks (RBFNNs), the locations of Gaussian neurons are commonly determined by clustering. Training inputs can be clustered on a fully unsupervised manner (input clustering), or some supervision can be introduced, for example, by concatenating the input vectors with weighted output vectors (input-output clustering). In this paper, we propose to apply clustering separately for each class (class-specific clustering). The idea has been used in some previous works, but without evaluating the benefits of the approach. We compare the class-specific, input, and input-output clustering approaches in terms of classification performance and computational efficiency when training RBFNNs. To accomplish this objective, we apply three different clustering algorithms and conduct experiments on 25 benchmark data sets. We show that the class-specific approach significantly reduces the overall complexity of the clustering, and our experimental results demonstrate that it can also lead to a significant gain in the classification performance, especially for the networks with a relatively few Gaussian neurons. Among other applied clustering algorithms, we combine, for the first time, a dynamic evolutionary optimization method, multidimensional particle swarm optimization, and the class-specific clustering to optimize the number of cluster centroids and their locations.
    Keywords: Training ; Neurons ; Clustering Algorithms ; Optimization ; Neural Networks ; Heuristic Algorithms ; Particle Swarm Optimization ; Clustering Methods ; Particle Swarm Optimization (Pso) ; Radial Basis Function Networks (Rbfnns) ; Supervised Learning ; Computer Science
    ISSN: 2162-237X
    E-ISSN: 2162-2388
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  • 9
    Language: English
    In: IEEE Transactions on Industrial Electronics, November 2016, Vol.63(11), pp.7067-7075
    Description: Early detection of the motor faults is essential and artificial neural networks are widely used for this purpose. The typical systems usually encapsulate two distinct blocks: feature extraction and classification. Such fixed and hand-crafted features may be a suboptimal choice and require a significant computational cost that will prevent their usage for real-time applications. In this paper, we propose a fast and accurate motor condition monitoring and early fault-detection system using 1-D convolutional neural networks that has an inherent adaptive design to fuse the feature extraction and classification phases of the motor fault detection into a single learning body. The proposed approach is directly applicable to the raw data (signal), and, thus, eliminates the need for a separate feature extraction algorithm resulting in more efficient systems in terms of both speed and hardware. Experimental results obtained using real motor data demonstrate the effectiveness of the proposed method for real-time motor condition monitoring.
    Keywords: Induction Motors ; Feature Extraction ; Fault Detection ; Convolution ; Real-Time Systems ; Neural Networks ; Mathematical Model ; Convolutional Neural Networks (Cnns) ; Motor Current Signature Analysis (Mcsa) ; Engineering
    ISSN: 0278-0046
    E-ISSN: 1557-9948
    Source: IEEE Conference Publications
    Source: IEEE Journals & Magazines 
    Source: IEEE Xplore
    Source: IEEE Journals & Magazines
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  • 10
    Description: Graph clustering is an important technique to understand the relationships between the vertices in a big graph. In this paper, we propose a novel random-walk-based graph clustering method. The proposed method restricts the reach of the walking agent using an inflation function and a normalization function. We analyze the behavior of the limited random walk procedure and propose a novel algorithm for both global and local graph clustering problems. Previous random-walk-based algorithms depend on the chosen fitness function to find the clusters around a seed vertex. The proposed algorithm tackles the problem in an entirely different manner. We use the limited random walk procedure to find attracting vertices in a graph and use them as features to cluster the vertices. According to the experimental results on the simulated graph data and the real-world big graph data, the proposed method is superior to the state-of-the-art methods in solving graph clustering problems. Since the proposed method uses the embarrassingly parallel paradigm, it can be efficiently implemented and embedded in any parallel computing environment such as a MapReduce framework. Given enough computing resources, we are capable of clustering graphs with millions of vertices and hundreds millions of edges in a reasonable time. Comment: 12 pages, 3 figures, 7 tables, journal paper
    Keywords: Computer Science - Social And Information Networks ; Physics - Physics And Society
    Source: Cornell University
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