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The label tree-based classification is one of the most popular approaches for reducing the testing complexity to sublinear with the large number of classes. One of the popular approaches to generate the label tree is to apply recursively a spectral clustering algorithm to an affinity matrix for partition set of class labels into subsets, each subset corresponds to a child node of the tree. To obtain...
The focus of this paper is on multitask learning over adaptive networks where different clusters of nodes have different objectives. We propose an adaptive regularized diffusion strategy using Gaussian kernel regularization to enable the agents to learn about the objectives of their neighbors and to ignore misleading information. In this way, the nodes will be able to meet their objectives more accurately...
Among the classification algorithms in machine learning, the KNN (K nearest neighbor) algorithm is one of the most frequent used methods for its characteristics of simplicity and efficiency. Even though KNN algorithm is very effective in many situations while it still has two shortcomings, not only is the efficiency of this classification algorithm obviously affected by redundant dimensional features,...
Self Organizing Maps perform clustering of data based on unsupervised learning. It is of concern that initialization of the weight vector contributes significantly to the performance of SOM and since real world datasets being high-dimensional, the complexity of SOM tend to increase tremendously leading to increased time consumption as well. Our work focuses on the analysis of different weight initialization...
Clustering of high dimensionality data which can be seen in almost all fields these days is becoming very tedious process. The key disadvantage of high dimensional data which we can pen down is curse of dimensionality. As the magnitude of datasets grows the data points become sparse and density of area becomes less making it difficult to cluster that data which further reduces the performance of traditional...
Application of clustering algorithms for investigating real life data has concerned many researchers and vague approaches or their hybridization with other analogous approaches has gained special attention due to their great effectiveness. Recently, rough intuitionistic fuzzy c-means algorithm has been proposed by Tripathy et al [3] and they established its supremacy over all other algorithms contained...
Kernel fuzzy clustering has been applied to data with nonlinear relationships. Two approaches were used: clustering with a single kernel and clustering with multiple kernels. While clustering with a single kernel doesn't work well with “multiple-density” clusters, Multiple Kernel Fuzzy clustering tries to find an optimal linear weighted combination of kernels with initial fixed (not necessarily the...
A novel fuzzy clustering algorithm is presented in this paper, which removes the constraints generally imposed to the cluster shape when a given model is adopted for membership functions. An on-line, sequential procedure is proposed where the cluster determination is performed by using suited membership functions based on geometrically unconstrained kernels and a point-to-shape distance evaluation...
The profusion of spectral bands generated by the acquisition process of hyperspectral images generally leads to high computational costs. Such difficulties arise in particular with nonlinear unmixing methods, which are naturally more complex than linear ones. This complexity, associated with the high redundancy of information within the complete set of bands, make the search of band selection algorithms...
In K-means clustering algorithm, the selection of cluster number k and initial K-means center has certain influence on the result. It would generate very different aggregation result when confronting with some certain types of data set. This paper aims at proposing an estimation method to evaluate the initial parameters for K-means algorithm. The estimation is executed through data analysis, which...
SAR image change detection is a fundamental process in many applications such as damage assessment, natural disasters monitoring and urban planning. Now as the scale of images and the complexity of algorithms rise, sequential methods have been more and more inefficient and powerless. In this paper, we propose a distributed parallel image change detection method based on Spark, an in-memory cluster...
Classical unmixing algorithms focus primarily on scenarios with a single mixture. These techniques are easily extensible in the case of images with multiple discrete mixtures (i.e. no shared endmembers). Unmixing in scenarios with multiple mixtures with shared or common endmembers is significantly harder. Manifold clustering and embedding seem tailor-made for such a scenario, but generally these algorithms...
This paper addresses the problem of bad data detection in the power grid. An online probability density based technique is presented to identify bad measurements within a sensor data stream in a decentralized manner using only the data from the neighboring buses and a one-hop communication system. Analyzing the spatial and temporal dependency between the measurements, the proposed algorithm identifies...
Spectral clustering has become one of the main clustering methods and has a wide range of applications. Similarity measure is crucial to correct cluster separation for spectral clustering. Many existing spectral clustering algorithms typically measure similarity based on the undirected k-Nearest Neighbor (kNN) graph or Gaussian kernel function, which can not reveal the real clusters of not well-separated...
Data processing is usually based on uniformly sampled data in time. This sampling scheme is often unnecessary for non-stationary signals because samples are also taken in inactive regions. In case of embedded system, this useless samples significantly increase the power consumption. One solution to avoid this useless power consumption is the level crossing sampling scheme (LCSS). This method is not...
Scene detection is a fundamental tool for allowing effective video browsing and re-using. In this paper we present a model that automatically divides videos into coherent scenes, which is based on a novel combination of local image descriptors and temporal clustering techniques. Experiments are performed to demonstrate the effectiveness of our approach, by comparing our algorithm against two recent...
We aim to understand and characterize embeddings of datasets with small anomalous clusters using the Laplacian Eigenmaps algorithm. To do this, we characterize the order in which eigenvectors of a disjoint graph Laplacian emerge and the support of those eigenvectors. We then extend this characterization to weakly connected graphs with clusters of differing sizes, utilizing the theory of invariant...
This paper aims mainly to improve the data analysis methods already used to detect introduers in [1]. To do that, we introduce two anomaly intrusion detection methods based on Kernel Fisher Discriminant Analysis (KFDA) and Kernel Principal Component Analysis (KPCA). This approach searches for those vectors in the underlying space that best discriminate among users' profile classes. The discrimination...
The ability to automatically detect faults or fault patterns to enhance system reliability is important for system administrators in reducing system failures. To achieve this objective, the message logs from cluster system are augmented with failure information, i.e., The raw log data is labelled. However, tagging or labelling of raw log data is very costly. In this paper, our objective is to detect...
Tile algorithms for matrix decomposition can generate many fine-grained tasks. Therefore, their suitability for processing with multicourse architecture has attracted much attention from the high-performance computing (HPC) community. Our implementation of tile QR decomposition for a cluster system has dynamic scheduling, OpenMP work- sharing, and other useful features. In this article, we discuss...
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