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An algorithm is proposed to solve the sensor subset selection problem. In this problem, a prespecified number of sensors are selected to estimate the value of a parameter such that a metric of estimation accuracy is maximized. The metric is defined as the determinant of the Bayesian Fisher information matrix (B-FIM). It is shown that the metric can be expanded as a homogenous polynomial of decision...
As a method built upon spectral graph theory, spectral clustering has the advantages of processing data with any spatial shapes and converging on global optimal solutions. But it suffers from the defects that the clustering result is quite sensitive to its parameters and the number of clusters must be prespecified. In this paper, a novel approach which integrates the grey relational analysis based...
Active learning and semi-supervised learning are both important techniques to improve the learned model using unlabeled data, when labeled data is difficult to obtain, and unlabeled data is available in large quantity and easy to collect. Combining active learning with a semi-supervised learning algorithm that uses Gaussian field and harmonic functions was suggested recently. This work showed that...
Node clustering has wide-ranging applications in decentralized P2P networks such as P2P file sharing systems, mobile ad-hoc networks, P2P sensor networks, and so forth. This paper proposes an approach to construct clusters in unstructured P2P networks based on small-world theory. In contrast to centralized graph clustering algorithms, our scheme is completely decentralized and it only uses the knowledge...
Fusion of multiple information sources can yield significant benefits to accomplishing certain learning tasks. This paper exploits the sparse representation of signals for the problem of data clustering. The method is built within the framework of spectral clustering algorithms, which convexly combines a real graph constructed from the given physical features with a virtual graph constructed from...
In this paper, an easily implemented semi-supervised graph learning method is presented for dimensionality reduction and clustering, using the most of prior knowledge from limited pairwise constraints. We extend instance-level constraints to space-level constraints to construct a more meaningful graph. By decomposing the (normalized) Laplacian matrix of this graph, to use the bottom eigenvectors leads...
Pattern recognition algorithms are facing the challenge to deal with an increasing number of complex objects. For graph data, a whole toolbox of pattern recognition algorithms becomes available by defining a kernel function on instances of graphs. Graph similarity is the central problem for all learning tasks such as clustering and classification on graphs. Graph kernels based on walks, shortest path,...
In the technique known as network coordinates, the network latency between nodes is modeled as the distance between points in a metric space. Actual network latencies, however, exhibit numerous triangle inequality violations, which result in significant error between the actual latency and the distance as determined by the network coordinates. In this work, we show how graph clustering techniques...
Graph or network clustering is one of the fundamental multimodal combinatorial problems that have many applications in computer science. Many algorithms have been devised to obtain a reasonable approximate solution for the problem. Current approaches, however, suffer from the local optimum drawback and then have difficulty splitting two clusters with very confused structures. In this paper we propose...
This paper describes that a graph-based co-clustering approach is suitable for extraction of verb synonyms from large scale texts. The proposed bipartite graph algorithm can produce clusters of verb synonyms as well as noun synonyms taking into account word co-occurrence between verb and its argument. Experimental results show that the co-clustering approach achieve higher accuracy than those by a...
In this work we present a novel method to model instance-level constraints within a clustering algorithm. Thereby, both similarity and dissimilarity constraints can be used coevally. The proposed extension is based on a distance transformation by shortest path computations in a constraint graph. With a new technique cannot-links are consistently supported and the dissimilarity is extended to their...
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